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Model: laion/a3-rl-DCAgent_exp_rpt_curriculum-medium-10-8B
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# a3-rl-DCAgent_exp_rpt_curriculum-medium-10-8B
RL (SkyRL/terminus-2) fine-tune of `laion/GLM-4_7-swesmith-sandboxes-with_tests-oracle_verified_120s-maxeps-131k-fixthink` on `DCAgent/exp_rpt_curriculum-medium` (512-task set, 2 epochs).
Checkpoint = **global_step_10**, selected by EMA-best of `reward/avg_raw_reward` (5-step EMA, α=1/3) across the full chain (job 591150). EMA at step 10 = 0.3106 (reward 0.2812). Data-limited run; training metrics emitted through step 16, checkpoints exported through step 21.
## Training Traces
Training-time Daytona/Harbor rollouts for this run are uploaded as a companion dataset:
**[penfever/a3-rl-DCAgent_exp_rpt_curriculum-medium](https://huggingface.co/datasets/penfever/a3-rl-DCAgent_exp_rpt_curriculum-medium)**
The dataset contains the `last` episode of each trial (per `make_and_upload_trace_dataset --episodes last`) — the same rollouts the policy was trained on after rollback / truncation.
## Training logs
See `training_logs/` for per-step metrics CSVs, the metrics report, and the reward-vs-steps plot (Jupiter has no W&B).

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{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0].role == 'system' %}
{{- messages[0].content + '\n\n' }}
{%- endif %}
{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
{%- else %}
{%- if messages[0].role == 'system' %}
{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
{%- for message in messages[::-1] %}
{%- set index = (messages|length - 1) - loop.index0 %}
{%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
{%- set ns.multi_step_tool = false %}
{%- set ns.last_query_index = index %}
{%- endif %}
{%- endfor %}
{%- for message in messages %}
{%- if message.content is string %}
{%- set content = message.content %}
{%- else %}
{%- set content = '' %}
{%- endif %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{%- set reasoning_content = '' %}
{%- if message.reasoning_content is string %}
{%- set reasoning_content = message.reasoning_content %}
{%- else %}
{%- if '</think>' in content %}
{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
{%- set content = content.split('</think>')[-1].lstrip('\n') %}
{%- endif %}
{%- endif %}
{%- if loop.index0 > ns.last_query_index %}
{%- if loop.last or (not loop.last and reasoning_content) %}
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- if message.tool_calls %}
{%- for tool_call in message.tool_calls %}
{%- if (loop.first and content) or (not loop.first) %}
{{- '\n' }}
{%- endif %}
{%- if tool_call.function %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{%- if tool_call.arguments is string %}
{{- tool_call.arguments }}
{%- else %}
{{- tool_call.arguments | tojson }}
{%- endif %}
{{- '}\n</tool_call>' }}
{%- endfor %}
{%- endif %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- content }}
{{- '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- if enable_thinking is defined and enable_thinking is false %}
{{- '<think>\n\n</think>\n\n' }}
{%- endif %}
{%- endif %}

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entrypoint: examples.terminal_bench.entrypoints.main_tbench
# Hydra config groups (+ prefix in CLI)
config_groups:
terminal_bench_config: terminal_bench
# Terminal bench / agentic environment settings
terminal_bench:
# trials_dir: Directory for Harbor trial artifacts (derived from experiments_dir if null)
trials_dir: null
# Harbor configuration - schema-driven mapping to TrialConfig
harbor:
# Agent settings
name: terminus-2
max_episodes: 999999
enable_summarize: false
store_all_messages: true
trajectory_config:
raw_content: true
enable_episode_logging: false
record_terminal_session: false
enable_pane_logging: false
# Strict JSON parser
strict_json_parser: true
# Interleaved Thinking Settings
interleaved_thinking: true
extra_body:
chat_template_kwargs:
enable_thinking: true
# 2026-05-27: 1800 → 900. The boundary-hugging a3 datasets (stack-junit,
# nemotron, methods2test) hit the 1800s agent-timeout wall on ~70-95% of
# trials (GLM-4_7-swesmith can't finish these in 1800s), so every group
# waited ~1800s for its slowest sample → generation buffer crawled (5/64
# in 4h19) and chains never reached training step 1. Halving to 900s
# ~2× the valid-group yield. Tradeoff: lower solve-rate on the hardest
# tasks (they fail faster) — acceptable vs. TIMEOUT-at-step-0. Fast-task
# chains (e2egit, median 7 turns / <900s) are unaffected. See
# agent_logs/2026-05-27_rl_chains_stuck_step0.md. AgentTimeoutError stays
# PASSTHROUGH (standing pref — partial trajectory+reward is meaningful).
override_timeout_sec: 900
# Environment settings
override_cpus: 1
override_memory_mb: 2048
override_storage_mb: 2048
# ==========================================================================
# AUTO SNAPSHOT: Reduce Daytona rate limits with hash-based snapshot caching
# ==========================================================================
# When true, automatically creates a snapshot from the Dockerfile on first use,
# then reuses it for all subsequent sandboxes with the same Dockerfile content.
# Snapshots are named: harbor__<sha256[:12]>__snapshot
auto_snapshot: true
# Verifier settings
verifier_override_timeout_sec: 120
# Retry settings
max_retries: 3
min_wait_sec: 60.0
max_wait_sec: 600.0
wait_multiplier: 2.0
exclude_exceptions:
- VerifierTimeoutError
- VerifierRuntimeError
- RewardFileNotFoundError
- RewardFileEmptyError
- VerifierOutputParseError
# 3x base (300 → 900): triple Daytona concurrency for higher throughput.
# Mirrors ALCC/56GPU_base.yaml — per-job sandbox count; doesn't affect
# the cross-cluster RUNNING-RL cap (≤ 6) which is about job count, not
# sandboxes-per-job.
# NOTE (2026-05-27): a temporary 900→500 walk-back was tried to mitigate
# the FD-exhaustion / uv__epoll_ctl_prep SIGABRT but REVERTED — the real
# fix is porting harbor's terminus-2 FD-taming features from
# penfever/temp-override into penfever/otagent-latest (that branch had the
# issue tamed at full concurrency). See agent_logs/2026-05-27_rl_chains_stuck_step0.md.
# 2026-05-28: 900 → 675 (-25%). Fresh a3 chains (#12/#13) abort with a
# driver SIGABRT that is NOT FD exhaustion (fd-monitor showed 1.6% of the
# 131072 limit) — suspected host-memory OOM from too many concurrent
# never-completing trials piling up resident state in skyrl_entrypoint on
# hard datasets. Cutting concurrent trial count reduces that resident
# memory. Paired with num_parallel_generation_workers -25%. See
# agent_logs/2026-05-28_fresh_a3_chain_crash_not_fd.md.
n_concurrent_trials: 675
# Logging settings
log_level: INFO
# Reward shaping (disabled - binary rewards)
enable_reward_shaping: false
# RLOO-N error classification
enable_error_classification: true
mask_exceptions:
- DaytonaError
- EnvironmentStartTimeoutError
- NetworkError
- ConnectionError
- RewardFileNotFoundError
- RewardFileEmptyError
- AgentEnvironmentTimeoutError
- ContextLengthExceededError
default_error_treatment: zero
# NOTE (2026-05-27): AgentTimeoutError / ContextLengthExceededError moved
# OUT of passthrough_exceptions into zero_exceptions. Passthrough routed
# these soft-limit trials through the *normal* (live-trajectory) path in
# terminal_bench_generator._process_trial_result, keeping the trial's
# dangling vLLM ObjectRefs + orphaned litellm async-callback closures
# alive in the training batch. That fed Ray's distributed-refcount race
# (reference_count.cc:1619 → SIGABRT / WorkerCrashedError) that killed the
# nemotron-junit chain (litellm +1 32769 VLLMValidationError +
# ContextLengthExceeded on near-budget prompts). Both mask + zero take the
# EARLY-RETURN path (response_ids=[0]) which never consumes rollout_details,
# so no dangling refs enter training. 2026-05-27: ContextLengthExceededError
# moved to mask_exceptions (above) — excluded from the RLOO-N baseline, since
# GLM-4_7-swesmith ~32k prompts overflow routinely and we don't want hard
# zeros dragging the baseline. AgentTimeoutError stays PASSTHROUGH (partial trajectory+reward is
# meaningful signal; infrequent vs ContextLengthExceeded so low refcount risk). Belt-and-suspenders w/ the _harbor_compat rollback_hook that
# already truncates these trials' dangling prompt-without-response.
passthrough_exceptions:
- AgentTimeoutError
zero_exceptions: []
# Model info for Harbor's hosted_vllm validation
model_info:
# Lowered 32767 -> 32000 (2026-05-27). Harbor's litellm token counter is
# +2 below vLLM's actual BPE tokenization on near-budget prompts (litellm
# +1 plus chat-template/special-token drift), so a 32767-capped prompt
# tokenizes to 32769 at the vLLM serving layer and is rejected with
# `VLLMValidationError: 32769 input tokens ... context length is only
# 32768`. vLLM returns this as an ErrorResponse (handled, not an engine
# crash), but harbor's litellm then retries the *deterministically*
# over-budget request (max_retries=3, exp backoff) across n_concurrent
# trials. On a boundary-heavy task distribution (e.g. nemotron-junit: 111+
# such overflows per chain link) the accumulating open sockets exhaust the
# skyrl_entrypoint actor's file descriptors -> libuv uv__epoll_ctl_prep
# aborts -> SIGABRT in the uvloop event loop -> ray.WorkerCrashedError kills
# the chain before training step 1 (job a3-rl ...nemotron-junit #11,
# chain 521442-448). A 768-token buffer below max_model_len=32768 ensures
# harbor truncates before vLLM ever rejects, eliminating the retry storm.
# Cost is ~2.3% usable context for all a3 chains; chains that don't hug the
# boundary (e2egit reached step 68 fine at 32767) are unaffected.
max_input_tokens: 32000
max_output_tokens: 4096
archiving:
# Enable trial archiving callback
enabled: false
# Post-training trace upload to HuggingFace
trace_upload:
enabled: true
repo_org: DCAgent
episodes: last
dataset_type: SFT
cleanup: true
# Trainer configuration
trainer:
strategy: fsdp2
algorithm:
advantage_estimator: rloo_n
use_kl_loss: false
kl_loss_coef: 0.0
eps_clip_low: 0.2
# eps_clip_high=0.05 mirrors 24GPU_base — midpoint between 0.2 default and
# 0.01 tight. Engages on collapse-onset ratios without over-clamping
# healthy updates. Asymmetric — only tightening upper bound.
eps_clip_high: 0.05
loss_reduction: token_mean
# Training loop settings
epochs: 2
max_steps: 80
update_epochs_per_batch: 1
# Batch sizes
train_batch_size: 64
policy_mini_batch_size: 64
eval_batch_size: 64
# Micro batch sizes (micro1x4 variant)
micro_forward_batch_size_per_gpu: 4
micro_train_batch_size_per_gpu: 1
max_prompt_length: 999999
# Evaluation and checkpointing
eval_interval: 999999
eval_before_train: false
# Resumable checkpointing
ckpt_interval: 2
resume_mode: latest
# HF upload-ready checkpoints
hf_save_interval: 5
# HuggingFace Hub upload (set via CLI: trainer.hf_hub_repo_id=org/repo)
hf_hub_repo_id: null
hf_hub_private: false
hf_hub_revision: main
# Database registration (auto-registers trained model to Supabase)
# Requires KEYS env var pointing to Supabase credentials file
enable_db_registration: false
# Logging
project_name: OpenThoughts-Agent
log_level: INFO
tracker_commit_each_step: true
logger: console
# Paths
run_name: null
ckpt_path: null
export_path: null
# Policy optimizer
# max_grad_norm=0.9 mirrors 24GPU_base — guardrail against grad-norm spikes
# entering correlation-mode-collapse territory (>1.0). 0.9 is just above the
# natural healthy peak observed on this dataset+base (‖g‖ peak ~0.81).
policy:
optimizer_config:
lr: 8e-6
weight_decay: 0.0
adam_betas: [0.9, 0.999]
max_grad_norm: 0.9
fsdp_config:
cpu_offload: false
reshard_after_forward: true
fsdp_size: 4
# Reference model
ref:
fsdp_config:
cpu_offload: false
reshard_after_forward: true
fsdp_size: 4
# Model placement (async training) - 8 shared GPUs for policy/ref
placement:
colocate_all: false
policy_num_nodes: 2
ref_num_nodes: 2
policy_num_gpus_per_node: 4
ref_num_gpus_per_node: 4
# Fully async generation (settings from v2_maxconcurrent)
fully_async:
max_staleness_steps: 16
# Setting conservatively to 1 / 2 of total concurrency
# 2026-05-28: 450 → 338 (-25%), paired with n_concurrent_trials 900→675,
# to cut driver memory pressure (suspected OOM on fresh hard-dataset a3
# chains). See agent_logs/2026-05-28_fresh_a3_chain_crash_not_fd.md.
num_parallel_generation_workers: 338
# Generator configuration
generator:
backend: vllm
timeout_multiplier: 1.0
model_dtype: bfloat16
inference_engine_tensor_parallel_size: 1
# 3x base (16 → 48): triple vLLM engines to lift gen throughput above training rate.
# Goal: build a real surplus of completed groups so the trainer never waits on gen.
# Layout: 48 engines × TP=1 + 8 GPUs for policy/ref = 56 GPUs total = 14 nodes.
num_inference_engines: 48
n_samples_per_prompt: 8
eval_n_samples_per_prompt: 8
# Jupiter-specific gpu_memory_utilization: 0.75 (vs ALCC's 0.85).
# GH200's 96 GB HBM has more headroom than ALCC's A100-80, but Jupiter
# also runs hosted_vllm in the same Ray cluster as the engines and we
# keep 0.75 across all jupiter yamls for consistency / fragmentation
# safety. Bump to 0.85 only if KV-cache pressure justifies it.
gpu_memory_utilization: 0.75
max_num_seqs: 24
# Jupiter-specific 65536 (vs ALCC's 16384) — matches the existing
# 24GPU_base.yaml on Jupiter; larger batched-token budget keeps the
# engine fed when many concurrent requests arrive in bursts.
max_num_batched_tokens: 65536
enable_prefix_caching: true
enable_chunked_prefill: true
run_engines_locally: true
weight_sync_backend: nccl
async_engine: true
batched: false
enable_http_endpoint: true
enable_ray_prometheus_stats: false
vllm_stats_interval: 1
append_eos_token_after_stop_str_in_multi_turn: true
max_turns: 999999
sampling_params:
max_generate_length: 4096
temperature: 0.7
top_p: 0.95
top_k: 20
engine_init_kwargs:
max_model_len: 32768
# Interleaved thinking chat template: preserves <think> blocks on ALL
# historical assistant turns (stock Qwen3 template strips them).
custom_chat_template_chat_completion_path: chat_templates/qwen3_thinking_acc.jinja2
# Data paths
data:
train_data: []
val_data: []

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special_tokens_map.json Normal file
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{
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tokenizer.json (Stored with Git LFS) Normal file

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tokenizer_config.json Normal file
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async/discard_rate,async/discarded_count,async/effective_batch_groups,async/effective_batch_samples,async/staleness_max,async/staleness_mean,async/staleness_min,async/staleness_ratio,generate/avg_num_tokens,generate/avg_tokens_non_zero_rewards,generate/avg_tokens_zero_rewards,generate/max_num_tokens,generate/min_num_tokens,generate/std_num_tokens,loss/avg_final_rewards,loss/avg_raw_advantages,loss/avg_raw_advantages_abs,policy/final_loss,policy/log_ratio_abs_max,policy/log_ratio_abs_mean,policy/log_ratio_abs_p99,policy/log_ratio_abs_pos00,policy/log_ratio_abs_pos10,policy/log_ratio_abs_pos20,policy/log_ratio_abs_pos30,policy/log_ratio_abs_pos40,policy/log_ratio_abs_pos50,policy/log_ratio_abs_pos60,policy/log_ratio_abs_pos70,policy/log_ratio_abs_pos80,policy/log_ratio_abs_pos90,policy/n_tokens_dp_gt_10pct,policy/n_tokens_dp_gt_1pct,policy/n_tokens_dp_gt_50pct,policy/policy_entropy,policy/policy_loss,policy/policy_lr,policy/policy_update_steps,policy/ppo_clip_ratio,policy/raw_grad_norm,reward/avg_pass_at_8,reward/avg_raw_reward,system/process_rss_gb,system/process_vms_gb,system/ram_available_gb,system/ram_percent,system/ram_total_gb,system/ram_used_gb,timing/compute_advantages_and_returns,timing/convert_to_training_input,timing/fwd_logprobs_values_reward,timing/policy_train,timing/run_training,timing/step,timing/sync_weights,timing/train_critic_and_policy,timing/wait_for_generation_buffer,trainer/epoch,trainer/global_step,batch_errors/total_batches,batch_errors/total_instances,batch_errors/total_successful,batch_errors/total_failed,batch_errors/total_masked,batch_errors/avg_ContextLengthExceededError,batch_errors/total_ContextLengthExceededError,timing/cleanup_old_checkpoints,timing/save_checkpoints,batch_errors/avg_DaytonaError,batch_errors/total_DaytonaError,batch_errors/avg_VerifierTimeoutError,batch_errors/total_VerifierTimeoutError,batch_errors/avg_InvalidChatHistory,batch_errors/total_InvalidChatHistory,batch_errors/avg_AgentTimeoutError,batch_errors/total_AgentTimeoutError,timing/save_hf_model,batch_errors/avg_RuntimeError,batch_errors/total_RuntimeError
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1 async/discard_rate async/discarded_count async/effective_batch_groups async/effective_batch_samples async/staleness_max async/staleness_mean async/staleness_min async/staleness_ratio generate/avg_num_tokens generate/avg_tokens_non_zero_rewards generate/avg_tokens_zero_rewards generate/max_num_tokens generate/min_num_tokens generate/std_num_tokens loss/avg_final_rewards loss/avg_raw_advantages loss/avg_raw_advantages_abs policy/final_loss policy/log_ratio_abs_max policy/log_ratio_abs_mean policy/log_ratio_abs_p99 policy/log_ratio_abs_pos00 policy/log_ratio_abs_pos10 policy/log_ratio_abs_pos20 policy/log_ratio_abs_pos30 policy/log_ratio_abs_pos40 policy/log_ratio_abs_pos50 policy/log_ratio_abs_pos60 policy/log_ratio_abs_pos70 policy/log_ratio_abs_pos80 policy/log_ratio_abs_pos90 policy/n_tokens_dp_gt_10pct policy/n_tokens_dp_gt_1pct policy/n_tokens_dp_gt_50pct policy/policy_entropy policy/policy_loss policy/policy_lr policy/policy_update_steps policy/ppo_clip_ratio policy/raw_grad_norm reward/avg_pass_at_8 reward/avg_raw_reward system/process_rss_gb system/process_vms_gb system/ram_available_gb system/ram_percent system/ram_total_gb system/ram_used_gb timing/compute_advantages_and_returns timing/convert_to_training_input timing/fwd_logprobs_values_reward timing/policy_train timing/run_training timing/step timing/sync_weights timing/train_critic_and_policy timing/wait_for_generation_buffer trainer/epoch trainer/global_step batch_errors/total_batches batch_errors/total_instances batch_errors/total_successful batch_errors/total_failed batch_errors/total_masked batch_errors/avg_ContextLengthExceededError batch_errors/total_ContextLengthExceededError timing/cleanup_old_checkpoints timing/save_checkpoints batch_errors/avg_DaytonaError batch_errors/total_DaytonaError batch_errors/avg_VerifierTimeoutError batch_errors/total_VerifierTimeoutError batch_errors/avg_InvalidChatHistory batch_errors/total_InvalidChatHistory batch_errors/avg_AgentTimeoutError batch_errors/total_AgentTimeoutError timing/save_hf_model batch_errors/avg_RuntimeError batch_errors/total_RuntimeError
2 0.0 0 64 512 0 0.0 0 0.0 8795.541 7557.8707 9821.0393 28234 1862 5511.4955 0.4531 0.0154 0.1383 -0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1147 -0.0 0.0 1.0 0.0 0.0154 0.6094 0.4531 12.2192 50.7338 643.6898 25.0 857.9676 214.2778 0.1433 4.903 67.5598 331.9725 399.8923 2717.3975 19.1362 332.1889 2293.4656 0 1 127.0 1016.0 978.0 16.0 38.0 0.31496062992125984 40.0
3 0.0 0 64 512 1 0.8906 0 0.8906 10265.4648 9975.6566 10448.2102 30188 1 7251.4743 0.3867 0.0046 0.1482 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1058 0.0 0.0 1.0 0.0 0.0194 0.5312 0.3867 12.8623 51.2017 641.1227 25.3 857.9676 216.8448 0.0832 5.4115 69.2781 417.5714 487.2024 1580.8868 19.4255 417.8407 1068.8446 0 2 61.0 488.0 289.0 42.0 199.0 3.278688524590164 200.0 10.894 19.6772 0.04918032786885246 3.0 0.03278688524590164 2.0 0.03278688524590164 2.0
4 0.0 0 64 512 2 1.7188 1 1.0 6598.4668 9521.9474 6088.8693 29419 1 7508.721 0.1484 0.014 0.1069 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0709 0.0 0.0 1.0 0.0 0.0109 0.2969 0.1484 12.9957 51.341 637.1793 25.7 857.9676 220.7883 0.0935 4.8247 53.3349 340.9789 394.6829 675.1796 19.7755 341.254 255.893 0 3 60.0 480.0 411.0 22.0 69.0 1.05 63.0 0.016666666666666666 1.0 0.016666666666666666 1.0
5 0.0 0 64 512 3 2.4219 1 1.0 9999.6543 9601.1462 10199.4927 29478 1 7901.9526 0.334 0.0083 0.0995 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1012 0.0 0.0 1.0 0.0 0.0119 0.4531 0.334 13.3472 51.7056 629.7285 26.6 857.9676 228.2391 0.0901 5.533 67.9116 401.1539 469.4069 1064.6399 19.7968 401.4046 569.8905 0 4 65.0 520.0 384.0 31.0 136.0 2.0153846153846153 131.0 0.0057 12.0885 0.07692307692307693 5.0
6 0.0 0 64 512 4 3.0625 2 1.0 9386.873 11132.7622 8710.2818 29967 1 8316.5867 0.2793 -0.0005 0.1511 -0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0843 -0.0 0.0 1.0 0.0 0.0126 0.4375 0.2793 13.3903 51.7529 626.9168 26.9 857.9676 231.0508 0.0708 5.2362 79.0552 433.729 513.1297 1514.2234 19.1602 434.0033 976.6598 0 5 62.0 496.0 384.0 32.0 112.0 1.9193548387096775 119.0 6.6576
7 0.0 0 64 512 4 3.7656 3 1.0 10051.041 12819.4872 8837.9017 29419 1 8180.7526 0.3047 0.0255 0.2105 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0891 0.0 0.0 1.0 0.0 0.0202 0.5469 0.3047 13.8707 51.8625 639.0039 25.5 857.9676 218.9637 0.077 5.2138 69.1587 408.4466 477.9141 961.7158 18.673 408.6779 459.8809 0 6 63.0 504.0 316.0 42.0 187.0 2.9365079365079363 185.0 0.0083 9.9275 0.031746031746031744 2.0 0.015873015873015872 1.0
8 0.0 0 64 512 5 4.7812 4 1.0 8289.875 10832.8 7553.2594 31013 1 8640.4314 0.2246 -0.0009 0.0888 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0742 0.0 0.0 1.0 0.0 0.0102 0.3281 0.2246 13.6701 51.6724 636.2298 25.8 857.9676 221.7378 0.0697 5.0448 66.0801 402.6137 469.0069 1264.3273 18.9369 402.8567 771.3001 0 7 60.0 480.0 268.0 53.0 211.0 3.45 207.0 0.03333333333333333 2.0 0.06666666666666667 4.0 0.016666666666666666 1.0
9 0.0 0 64 512 6 5.7656 5 1.0 8230.7988 14968.2024 6908.5047 29456 1 9244.7451 0.1641 0.0138 0.1045 -0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0659 -0.0 0.0 1.0 0.0 0.0139 0.3438 0.1641 14.0063 52.0513 628.7164 26.7 857.9676 229.2512 0.065 4.8541 69.1673 417.4759 486.978 2605.5652 11.9483 417.7453 2101.7529 0 8 64.0 512.0 487.0 7.0 25.0 0.390625 25.0 0.008 10.0222
10 0.0 0 64 512 0 0.0 0 0.0 10444.8711 9636.092 11216.6069 30180 1 6434.7455 0.4883 0.0112 0.1585 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1128 0.0 0.0 1.0 0.0 0.0149 0.6562 0.4883 13.9713 52.0759 624.9913 27.2 857.9676 232.9763 0.0687 5.276 68.7787 378.0224 447.1007 3884.0127 18.4616 378.2529 3413.142 1 9 58.0 464.0 301.0 36.0 163.0 3.0689655172413794 178.0 0.05172413793103448 3.0
11 0.0 0 64 512 1 1.0 1 1.0 8861.4961 11804.4167 7709.9185 29409 1 8814.3882 0.2812 0.0029 0.0973 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0734 0.0 0.0 1.0 0.0 0.0095 0.4062 0.2812 14.7997 52.574 627.1608 26.9 857.9676 230.8068 0.0644 5.1711 72.0844 417.3814 489.7789 926.0357 19.0802 417.6298 412.0023 1 10 64.0 512.0 332.0 36.0 179.0 2.640625 169.0 0.0076 9.5829 0.046875 3.0 0.015625 1.0 0.015625 1.0 0.03125 2.0 6.3468
12 0.0 0 64 512 2 1.7031 0 0.9531 7327.1992 9194.4762 6845.4693 28487 1 7502.5285 0.2051 0.0026 0.0657 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0804 0.0 0.0 1.0 0.0 0.01 0.2812 0.2051 15.1613 52.9576 624.4334 27.2 857.9676 233.5342 0.0601 4.715 63.8687 351.7918 415.97 995.055 19.1877 352.0408 555.1789 1 11 62.0 496.0 358.0 30.0 138.0 2.193548387096774 136.0 0.03225806451612903 2.0
13 0.0 0 64 512 3 2.0938 1 1.0 9918.1191 11967.1758 8943.7839 30360 1 8420.1283 0.3223 0.0095 0.1672 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0848 0.0 0.0 1.0 0.0 0.0144 0.4844 0.3223 15.4885 53.2342 634.7371 26.0 857.9676 223.2305 0.0831 5.3606 76.0698 426.1412 502.5426 1539.2977 19.1177 426.3893 1012.2655 1 12 62.0 496.0 314.0 40.0 181.0 2.8870967741935485 179.0 0.0072 9.3904 0.06451612903225806 4.0 0.016129032258064516 1.0
14 0.0 0 64 512 4 2.5938 2 1.0 9382.0039 12392.648 8409.5736 30099 1 9042.8211 0.2441 -0.0046 0.1564 -0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0751 -0.002 0.0 1.0 0.0 0.0148 0.4219 0.2441 15.3411 53.0607 632.1736 26.3 857.9676 225.7939 0.0688 5.2259 70.3939 431.8179 502.543 1232.3428 19.0569 432.0799 705.4787 1 13 64.0 512.0 303.0 44.0 209.0 3.203125 205.0 0.0625 4.0
15 0.0 0 64 512 4 3.0469 3 1.0 8458.4961 12622.1583 7183.9056 30096 1 8927.1653 0.2344 -0.0072 0.1074 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0716 0.0 0.0 1.0 0.0 0.0087 0.375 0.2344 15.6031 53.179 628.9873 26.7 857.9676 228.9803 0.0787 5.1752 73.5778 416.5122 490.4141 1429.1938 19.0701 416.7571 914.4966 1 14 60.0 480.0 334.0 33.0 146.0 2.716666666666667 163.0 0.0072 9.1814 0.03333333333333333 2.0
16 0.0 0 64 512 4 4.0 4 1.0 9658.8066 12490.4551 8288.1246 29820 1 8982.0547 0.3262 0.0033 0.186 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0761 0.0 0.0 1.0 0.0 0.0122 0.4688 0.3262 15.4732 53.0505 626.9476 26.9 857.9676 231.02 0.0661 5.096 76.3654 444.7385 521.4854 1171.3137 18.8645 445.0534 625.8348 1 15 63.0 504.0 225.0 54.0 278.0 4.349206349206349 274.0 0.06349206349206349 4.0 5.8824 0.015873015873015872 1.0
17 0.0 0 64 512 5 5.0 5 1.0 7392.418 16034.6364 6113.5247 29621 1 9341.5481 0.1289 -0.0013 0.1187 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0553 0.0 0.0 1.0 0.0 0.0095 0.2812 0.1289 15.7165 53.3323 624.8472 27.2 857.9676 233.1204 0.0635 4.8164 68.4974 418.1021 486.9176 2531.0981 13.3106 418.3564 2026.0146 1 16 0.0074 9.3875

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# SkyRL Training Metrics Analysis
Generated from 1 log files
## Overview
| Log File | Total Steps | Metric Blocks | Final Reward (mean) | Final Reward (max) | Total Time (s) |
|----------|-------------|---------------|---------------------|-------------------|----------------|
| job_591150 | 16 | 16 | 0.2828 | 0.4883 | 26092.3 |
## Async Metrics
| | Mean | Std | Min | Max | Count |
|:------------------------------|-----------:|---------:|------:|---------:|--------:|
| async/discard_rate | 0 | 0 | 0 | 0 | 16 |
| async/discarded_count | 0 | 0 | 0 | 0 | 16 |
| async/effective_batch_groups | 64 | 0 | 64 | 64 | 16 |
| async/effective_batch_samples | 512 | 0 | 512 | 512 | 16 |
| async/staleness_max | 3 | 1.82574 | 0 | 6 | 16 |
| async/staleness_mean | 2.61524 | 1.73471 | 0 | 5.7656 | 16 |
| async/staleness_min | 2 | 1.78885 | 0 | 5 | 16 |
| async/staleness_ratio | 0.865231 | 0.338976 | 0 | 1 | 16 |
## Generate Metrics
| | Mean | Std | Min | Max | Count |
|:-------------------------------------|----------:|---------:|---------:|---------:|--------:|
| generate/avg_num_tokens | 8941.32 | 1152.12 | 6598.47 | 10444.9 | 16 |
| generate/avg_tokens_non_zero_rewards | 11409.5 | 2195.47 | 7557.87 | 16034.6 | 16 |
| generate/avg_tokens_zero_rewards | 8329.9 | 1544.59 | 6088.87 | 11216.6 | 16 |
| generate/max_num_tokens | 29702.9 | 683.514 | 28234 | 31013 | 16 |
| generate/min_num_tokens | 117.312 | 465.25 | 1 | 1862 | 16 |
| generate/std_num_tokens | 8126.35 | 1067.38 | 5511.5 | 9341.55 | 16 |
## Loss Metrics
| | Mean | Std | Min | Max | Count |
|:----------------------------|----------:|-----------:|--------:|-------:|--------:|
| loss/avg_final_rewards | 0.282837 | 0.102531 | 0.1289 | 0.4883 | 16 |
| loss/avg_raw_advantages | 0.0060375 | 0.00855639 | -0.0072 | 0.0255 | 16 |
| loss/avg_raw_advantages_abs | 0.131562 | 0.0391317 | 0.0657 | 0.2105 | 16 |
## Policy Metrics
| | Mean | Std | Min | Max | Count |
|:----------------------------|-----------:|-----------:|--------:|-------:|--------:|
| policy/final_loss | 0 | 0 | -0 | 0 | 16 |
| policy/log_ratio_abs_max | 0 | 0 | 0 | 0 | 16 |
| policy/log_ratio_abs_mean | 0 | 0 | 0 | 0 | 16 |
| policy/log_ratio_abs_p99 | 0 | 0 | 0 | 0 | 16 |
| policy/log_ratio_abs_pos00 | 0 | 0 | 0 | 0 | 16 |
| policy/log_ratio_abs_pos10 | 0 | 0 | 0 | 0 | 16 |
| policy/log_ratio_abs_pos20 | 0 | 0 | 0 | 0 | 16 |
| policy/log_ratio_abs_pos30 | 0 | 0 | 0 | 0 | 16 |
| policy/log_ratio_abs_pos40 | 0 | 0 | 0 | 0 | 16 |
| policy/log_ratio_abs_pos50 | 0 | 0 | 0 | 0 | 16 |
| policy/log_ratio_abs_pos60 | 0 | 0 | 0 | 0 | 16 |
| policy/log_ratio_abs_pos70 | 0 | 0 | 0 | 0 | 16 |
| policy/log_ratio_abs_pos80 | 0 | 0 | 0 | 0 | 16 |
| policy/log_ratio_abs_pos90 | 0 | 0 | 0 | 0 | 16 |
| policy/n_tokens_dp_gt_10pct | 0 | 0 | 0 | 0 | 16 |
| policy/n_tokens_dp_gt_1pct | 0 | 0 | 0 | 0 | 16 |
| policy/n_tokens_dp_gt_50pct | 0 | 0 | 0 | 0 | 16 |
| policy/policy_entropy | 0.083475 | 0.0171367 | 0.0553 | 0.1147 | 16 |
| policy/policy_loss | -0.000125 | 0.0005 | -0.002 | 0 | 16 |
| policy/policy_lr | 0 | 0 | 0 | 0 | 16 |
| policy/policy_update_steps | 1 | 0 | 1 | 1 | 16 |
| policy/ppo_clip_ratio | 0 | 0 | 0 | 0 | 16 |
| policy/raw_grad_norm | 0.0130313 | 0.00340993 | 0.0087 | 0.0202 | 16 |
## Reward Metrics
| | Mean | Std | Min | Max | Count |
|:----------------------|---------:|---------:|-------:|-------:|--------:|
| reward/avg_pass_at_8 | 0.432613 | 0.114214 | 0.2812 | 0.6562 | 16 |
| reward/avg_raw_reward | 0.282837 | 0.102531 | 0.1289 | 0.4883 | 16 |
## System Metrics
| | Mean | Std | Min | Max | Count |
|:------------------------|---------:|---------:|---------:|---------:|--------:|
| system/process_rss_gb | 14.2448 | 1.12927 | 12.2192 | 15.7165 | 16 |
| system/process_vms_gb | 52.2366 | 0.825632 | 50.7338 | 53.3323 | 16 |
| system/ram_available_gb | 631.679 | 6.2014 | 624.433 | 643.69 | 16 |
| system/ram_percent | 26.3687 | 0.72454 | 25 | 27.2 | 16 |
| system/ram_total_gb | 857.968 | 0 | 857.968 | 857.968 | 16 |
| system/ram_used_gb | 226.288 | 6.20141 | 214.278 | 233.534 | 16 |
## Timing Metrics
| | Mean | Std | Min | Max | Count |
|:--------------------------------------|------------:|------------:|---------:|----------:|--------:|
| timing/compute_advantages_and_returns | 0.077875 | 0.0200385 | 0.0601 | 0.1433 | 16 |
| timing/convert_to_training_input | 5.11608 | 0.237222 | 4.715 | 5.533 | 16 |
| timing/fwd_logprobs_values_reward | 69.4489 | 5.87917 | 53.3349 | 79.0552 | 16 |
| timing/policy_train | 402.403 | 33.9185 | 331.973 | 444.738 | 16 |
| timing/run_training | 472.185 | 38.58 | 394.683 | 521.485 | 16 |
| timing/step | 1630.77 | 862.939 | 675.18 | 3884.01 | 16 |
| timing/sync_weights | 18.3126 | 2.25802 | 11.9483 | 19.7968 | 16 |
| timing/train_critic_and_policy | 402.658 | 33.9295 | 332.189 | 445.053 | 16 |
| timing/wait_for_generation_buffer | 1135.13 | 869.349 | 255.893 | 3413.14 | 16 |
| timing/cleanup_old_checkpoints | 1.36818 | 3.84901 | 0.0057 | 10.894 | 8 |
| timing/save_checkpoints | 11.1572 | 3.56358 | 9.1814 | 19.6772 | 8 |
| timing/save_hf_model | 6.2956 | 0.390128 | 5.8824 | 6.6576 | 3 |
## Trainer Metrics
| | Mean | Std | Min | Max | Count |
|:--------------------|-------:|---------:|------:|------:|--------:|
| trainer/epoch | 0.5 | 0.516398 | 0 | 1 | 16 |
| trainer/global_step | 8.5 | 4.76095 | 1 | 16 | 16 |
## Batch_Errors Metrics
| | Mean | Std | Min | Max | Count |
|:----------------------------------------------|------------:|--------------:|------------:|-------------:|--------:|
| batch_errors/total_batches | 66.3333 | 16.8932 | 58 | 127 | 15 |
| batch_errors/total_instances | 530.667 | 135.146 | 464 | 1016 | 15 |
| batch_errors/total_successful | 378.933 | 177.363 | 225 | 978 | 15 |
| batch_errors/total_failed | 34.5333 | 12.6935 | 7 | 54 | 15 |
| batch_errors/total_masked | 151.4 | 68.5709 | 25 | 278 | 15 |
| batch_errors/avg_ContextLengthExceededError | 2.42765 | 1.13601 | 0.314961 | 4.34921 | 15 |
| batch_errors/total_ContextLengthExceededError | 151.6 | 67.7725 | 25 | 274 | 15 |
| batch_errors/avg_DaytonaError | 0.0485792 | 0.0132974 | 0.0322581 | 0.0645161 | 9 |
| batch_errors/total_DaytonaError | 3 | 0.866025 | 2 | 4 | 9 |
| batch_errors/avg_VerifierTimeoutError | 0.0215136 | 0.00976617 | 0.015625 | 0.0327869 | 3 |
| batch_errors/total_VerifierTimeoutError | 1.33333 | 0.57735 | 1 | 2 | 3 |
| batch_errors/avg_InvalidChatHistory | 0.0400691 | 0.0258191 | 0.015625 | 0.0769231 | 6 |
| batch_errors/total_InvalidChatHistory | 2.5 | 1.64317 | 1 | 5 | 6 |
| batch_errors/avg_AgentTimeoutError | 0.0239583 | 0.010312 | 0.0166667 | 0.03125 | 2 |
| batch_errors/total_AgentTimeoutError | 1.5 | 0.707107 | 1 | 2 | 2 |
| batch_errors/avg_RuntimeError | 0.0161376 | 0.000458214 | 0.015873 | 0.0166667 | 3 |
| batch_errors/total_RuntimeError | 1 | 0 | 1 | 1 | 3 |
## Training Progression by Log
### job_591150
| Step | Reward | Pass@8 | KL | Loss | Step Time (s) | Gen Wait (s) |
|------|--------|--------|-----|------|---------------|-------------|
| 1 | 0.4531 | 0.6094 | 0.000000 | -0.0000 | 2717.4 | 2293.5 |
| 2 | 0.3867 | 0.5312 | 0.000000 | 0.0000 | 1580.9 | 1068.8 |
| 3 | 0.1484 | 0.2969 | 0.000000 | 0.0000 | 675.2 | 255.9 |
| 4 | 0.3340 | 0.4531 | 0.000000 | 0.0000 | 1064.6 | 569.9 |
| 5 | 0.2793 | 0.4375 | 0.000000 | -0.0000 | 1514.2 | 976.7 |
| 6 | 0.3047 | 0.5469 | 0.000000 | 0.0000 | 961.7 | 459.9 |
| 7 | 0.2246 | 0.3281 | 0.000000 | 0.0000 | 1264.3 | 771.3 |
| 8 | 0.1641 | 0.3438 | 0.000000 | -0.0000 | 2605.6 | 2101.8 |
| 9 | 0.4883 | 0.6562 | 0.000000 | 0.0000 | 3884.0 | 3413.1 |
| 10 | 0.2812 | 0.4062 | 0.000000 | 0.0000 | 926.0 | 412.0 |
| 11 | 0.2051 | 0.2812 | 0.000000 | 0.0000 | 995.1 | 555.2 |
| 12 | 0.3223 | 0.4844 | 0.000000 | 0.0000 | 1539.3 | 1012.3 |
| 13 | 0.2441 | 0.4219 | 0.000000 | -0.0000 | 1232.3 | 705.5 |
| 14 | 0.2344 | 0.3750 | 0.000000 | 0.0000 | 1429.2 | 914.5 |
| 15 | 0.3262 | 0.4688 | 0.000000 | 0.0000 | 1171.3 | 625.8 |
| 16 | 0.1289 | 0.2812 | 0.000000 | 0.0000 | 2531.1 | 2026.0 |
## Timing Analysis
### Average Time Breakdown (% of step time)
| Component | Avg % of Step Time |
|-----------|-------------------|
| wait_for_generation_buffer | 62.9% |
| run_training | 35.3% |
| train_critic_and_policy | 30.2% |
| policy_train | 30.1% |
| fwd_logprobs_values_reward | 5.2% |
| sync_weights | 1.4% |
| save_checkpoints | 0.8% |
| save_hf_model | 0.5% |
| convert_to_training_input | 0.4% |
| cleanup_old_checkpoints | 0.1% |
| compute_advantages_and_returns | 0.0% |
## vLLM Inference Engine Analysis
Metrics from vLLM stat loggers (V1LoggingStatLoggerFixed).
> **Note**: Ray deduplicates similar log messages with `[repeated Nx across cluster]`,
> so we typically capture stats from one engine per timestamp. The stats shown are
> **per-engine** values. Multiply by num_inference_engines for cluster-wide estimates.
### Summary by Log (Per-Engine Stats)
| Log | Avg Running/Engine | Avg Waiting/Engine | Avg Gen Throughput/Engine | Avg KV Cache % | Avg Prefix Hit % |
|-----|-------------------|-------------------|--------------------------|----------------|------------------|
| job_591150 | 6.4 | 0.0 | 287.6 tok/s | 29.4% | 90.9% |
### Utilization Analysis (Per-Engine)
Key indicators of inference engine utilization:
- **Running requests/engine**: Concurrent requests being processed by each engine
- **Waiting requests**: Requests queued (0 = engine not saturated, has spare capacity)
- **Generation throughput**: Decode tokens/sec per engine
- 8B model on H100 can do **1000+ tok/s** when saturated
- If seeing <300 tok/s with 0 waiting, engine is **starved for requests**
#### job_591150
- **Running requests/engine**: avg=6.4, max=19
- **Waiting requests**: avg=0.0, max=0
- **Generation throughput/engine**: avg=287.6 tok/s, max=720.3 tok/s
- **KV cache usage**: avg=29.4%
- **Prefix cache hit rate**: avg=90.9%
- **Low throughput**: 288 tok/s << expected 1000+ tok/s for saturated 8B model
## Trial-Level Analysis (from result.json)
Total trials parsed: 8192
### Turn Count Statistics
| Metric | Value |
|--------|-------|
| Mean | 11.8 |
| Median | 10.0 |
| Std | 6.7 |
| Min | 1 |
| Max | 151 |
| Count | 8185 |
### Exception Distribution
| Exception Type | Count | % |
|---------------|-------|---|
| No exception | 5561 | 67.9% |
| ContextLengthExceededError | 2311 | 28.2% |
| AgentTimeoutError | 286 | 3.5% |
| DaytonaError | 27 | 0.3% |
| VerifierTimeoutError | 4 | 0.0% |
| RuntimeError | 3 | 0.0% |
### Turn Count by Exception Type
| Exception Type | Mean Turns | Median Turns | Count |
|---------------|-----------|-------------|-------|
| VerifierTimeoutError | 17.5 | 15.5 | 4 |
| ContextLengthExceededError | 13.9 | 12.0 | 2311 |
| No exception | 11.0 | 10.0 | 5561 |
| RuntimeError | 10.3 | 10.0 | 3 |
| AgentTimeoutError | 10.2 | 7.0 | 286 |
| DaytonaError | 10.1 | 9.0 | 20 |
### Turn Count by Outcome
| Outcome | Mean Turns | Median Turns | Count |
|---------|-----------|-------------|-------|
| Success | 10.0 | 9.0 | 2317 |
| Failure | 11.6 | 10.0 | 3527 |
### Reward Summary
- Mean reward: 0.3965
- Success rate: 39.6%
- Trials with reward data: 5844

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@@ -0,0 +1,17 @@
log_file,async/discard_rate,async/discarded_count,async/effective_batch_groups,async/effective_batch_samples,async/staleness_max,async/staleness_mean,async/staleness_min,async/staleness_ratio,generate/avg_num_tokens,generate/avg_tokens_non_zero_rewards,generate/avg_tokens_zero_rewards,generate/max_num_tokens,generate/min_num_tokens,generate/std_num_tokens,loss/avg_final_rewards,loss/avg_raw_advantages,loss/avg_raw_advantages_abs,policy/final_loss,policy/log_ratio_abs_max,policy/log_ratio_abs_mean,policy/log_ratio_abs_p99,policy/log_ratio_abs_pos00,policy/log_ratio_abs_pos10,policy/log_ratio_abs_pos20,policy/log_ratio_abs_pos30,policy/log_ratio_abs_pos40,policy/log_ratio_abs_pos50,policy/log_ratio_abs_pos60,policy/log_ratio_abs_pos70,policy/log_ratio_abs_pos80,policy/log_ratio_abs_pos90,policy/n_tokens_dp_gt_10pct,policy/n_tokens_dp_gt_1pct,policy/n_tokens_dp_gt_50pct,policy/policy_entropy,policy/policy_loss,policy/policy_lr,policy/policy_update_steps,policy/ppo_clip_ratio,policy/raw_grad_norm,reward/avg_pass_at_8,reward/avg_raw_reward,system/process_rss_gb,system/process_vms_gb,system/ram_available_gb,system/ram_percent,system/ram_total_gb,system/ram_used_gb,timing/compute_advantages_and_returns,timing/convert_to_training_input,timing/fwd_logprobs_values_reward,timing/policy_train,timing/run_training,timing/step,timing/sync_weights,timing/train_critic_and_policy,timing/wait_for_generation_buffer,trainer/epoch,trainer/global_step,batch_errors/total_batches,batch_errors/total_instances,batch_errors/total_successful,batch_errors/total_failed,batch_errors/total_masked,batch_errors/avg_ContextLengthExceededError,batch_errors/total_ContextLengthExceededError,timing/cleanup_old_checkpoints,timing/save_checkpoints,batch_errors/avg_DaytonaError,batch_errors/total_DaytonaError,batch_errors/avg_VerifierTimeoutError,batch_errors/total_VerifierTimeoutError,batch_errors/avg_InvalidChatHistory,batch_errors/total_InvalidChatHistory,batch_errors/avg_AgentTimeoutError,batch_errors/total_AgentTimeoutError,timing/save_hf_model,batch_errors/avg_RuntimeError,batch_errors/total_RuntimeError,global_step
job_591150,0.0,0,64,512,0,0.0,0,0.0,8795.541,7557.8707,9821.0393,28234,1862,5511.4955,0.4531,0.0154,0.1383,-0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1147,-0.0,0.0,1.0,0.0,0.0154,0.6094,0.4531,12.2192,50.7338,643.6898,25.0,857.9676,214.2778,0.1433,4.903,67.5598,331.9725,399.8923,2717.3975,19.1362,332.1889,2293.4656,0,1,127.0,1016.0,978.0,16.0,38.0,0.31496062992125984,40.0,,,,,,,,,,,,,,1
job_591150,0.0,0,64,512,1,0.8906,0,0.8906,10265.4648,9975.6566,10448.2102,30188,1,7251.4743,0.3867,0.0046,0.1482,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1058,0.0,0.0,1.0,0.0,0.0194,0.5312,0.3867,12.8623,51.2017,641.1227,25.3,857.9676,216.8448,0.0832,5.4115,69.2781,417.5714,487.2024,1580.8868,19.4255,417.8407,1068.8446,0,2,61.0,488.0,289.0,42.0,199.0,3.278688524590164,200.0,10.894,19.6772,0.04918032786885246,3.0,0.03278688524590164,2.0,0.03278688524590164,2.0,,,,,,2
job_591150,0.0,0,64,512,2,1.7188,1,1.0,6598.4668,9521.9474,6088.8693,29419,1,7508.721,0.1484,0.014,0.1069,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0709,0.0,0.0,1.0,0.0,0.0109,0.2969,0.1484,12.9957,51.341,637.1793,25.7,857.9676,220.7883,0.0935,4.8247,53.3349,340.9789,394.6829,675.1796,19.7755,341.254,255.893,0,3,60.0,480.0,411.0,22.0,69.0,1.05,63.0,,,,,,,0.016666666666666666,1.0,0.016666666666666666,1.0,,,,3
job_591150,0.0,0,64,512,3,2.4219,1,1.0,9999.6543,9601.1462,10199.4927,29478,1,7901.9526,0.334,0.0083,0.0995,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1012,0.0,0.0,1.0,0.0,0.0119,0.4531,0.334,13.3472,51.7056,629.7285,26.6,857.9676,228.2391,0.0901,5.533,67.9116,401.1539,469.4069,1064.6399,19.7968,401.4046,569.8905,0,4,65.0,520.0,384.0,31.0,136.0,2.0153846153846153,131.0,0.0057,12.0885,,,,,0.07692307692307693,5.0,,,,,,4
job_591150,0.0,0,64,512,4,3.0625,2,1.0,9386.873,11132.7622,8710.2818,29967,1,8316.5867,0.2793,-0.0005,0.1511,-0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0843,-0.0,0.0,1.0,0.0,0.0126,0.4375,0.2793,13.3903,51.7529,626.9168,26.9,857.9676,231.0508,0.0708,5.2362,79.0552,433.729,513.1297,1514.2234,19.1602,434.0033,976.6598,0,5,62.0,496.0,384.0,32.0,112.0,1.9193548387096775,119.0,,,,,,,,,,,6.6576,,,5
job_591150,0.0,0,64,512,4,3.7656,3,1.0,10051.041,12819.4872,8837.9017,29419,1,8180.7526,0.3047,0.0255,0.2105,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0891,0.0,0.0,1.0,0.0,0.0202,0.5469,0.3047,13.8707,51.8625,639.0039,25.5,857.9676,218.9637,0.077,5.2138,69.1587,408.4466,477.9141,961.7158,18.673,408.6779,459.8809,0,6,63.0,504.0,316.0,42.0,187.0,2.9365079365079363,185.0,0.0083,9.9275,,,,,0.031746031746031744,2.0,,,,0.015873015873015872,1.0,6
job_591150,0.0,0,64,512,5,4.7812,4,1.0,8289.875,10832.8,7553.2594,31013,1,8640.4314,0.2246,-0.0009,0.0888,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0742,0.0,0.0,1.0,0.0,0.0102,0.3281,0.2246,13.6701,51.6724,636.2298,25.8,857.9676,221.7378,0.0697,5.0448,66.0801,402.6137,469.0069,1264.3273,18.9369,402.8567,771.3001,0,7,60.0,480.0,268.0,53.0,211.0,3.45,207.0,,,0.03333333333333333,2.0,,,0.06666666666666667,4.0,,,,0.016666666666666666,1.0,7
job_591150,0.0,0,64,512,6,5.7656,5,1.0,8230.7988,14968.2024,6908.5047,29456,1,9244.7451,0.1641,0.0138,0.1045,-0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0659,-0.0,0.0,1.0,0.0,0.0139,0.3438,0.1641,14.0063,52.0513,628.7164,26.7,857.9676,229.2512,0.065,4.8541,69.1673,417.4759,486.978,2605.5652,11.9483,417.7453,2101.7529,0,8,64.0,512.0,487.0,7.0,25.0,0.390625,25.0,0.008,10.0222,,,,,,,,,,,,8
job_591150,0.0,0,64,512,0,0.0,0,0.0,10444.8711,9636.092,11216.6069,30180,1,6434.7455,0.4883,0.0112,0.1585,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1128,0.0,0.0,1.0,0.0,0.0149,0.6562,0.4883,13.9713,52.0759,624.9913,27.2,857.9676,232.9763,0.0687,5.276,68.7787,378.0224,447.1007,3884.0127,18.4616,378.2529,3413.142,1,9,58.0,464.0,301.0,36.0,163.0,3.0689655172413794,178.0,,,0.05172413793103448,3.0,,,,,,,,,,9
job_591150,0.0,0,64,512,1,1.0,1,1.0,8861.4961,11804.4167,7709.9185,29409,1,8814.3882,0.2812,0.0029,0.0973,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0734,0.0,0.0,1.0,0.0,0.0095,0.4062,0.2812,14.7997,52.574,627.1608,26.9,857.9676,230.8068,0.0644,5.1711,72.0844,417.3814,489.7789,926.0357,19.0802,417.6298,412.0023,1,10,64.0,512.0,332.0,36.0,179.0,2.640625,169.0,0.0076,9.5829,0.046875,3.0,0.015625,1.0,0.015625,1.0,0.03125,2.0,6.3468,,,10
job_591150,0.0,0,64,512,2,1.7031,0,0.9531,7327.1992,9194.4762,6845.4693,28487,1,7502.5285,0.2051,0.0026,0.0657,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0804,0.0,0.0,1.0,0.0,0.01,0.2812,0.2051,15.1613,52.9576,624.4334,27.2,857.9676,233.5342,0.0601,4.715,63.8687,351.7918,415.97,995.055,19.1877,352.0408,555.1789,1,11,62.0,496.0,358.0,30.0,138.0,2.193548387096774,136.0,,,0.03225806451612903,2.0,,,,,,,,,,11
job_591150,0.0,0,64,512,3,2.0938,1,1.0,9918.1191,11967.1758,8943.7839,30360,1,8420.1283,0.3223,0.0095,0.1672,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0848,0.0,0.0,1.0,0.0,0.0144,0.4844,0.3223,15.4885,53.2342,634.7371,26.0,857.9676,223.2305,0.0831,5.3606,76.0698,426.1412,502.5426,1539.2977,19.1177,426.3893,1012.2655,1,12,62.0,496.0,314.0,40.0,181.0,2.8870967741935485,179.0,0.0072,9.3904,0.06451612903225806,4.0,0.016129032258064516,1.0,,,,,,,,12
job_591150,0.0,0,64,512,4,2.5938,2,1.0,9382.0039,12392.648,8409.5736,30099,1,9042.8211,0.2441,-0.0046,0.1564,-0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0751,-0.002,0.0,1.0,0.0,0.0148,0.4219,0.2441,15.3411,53.0607,632.1736,26.3,857.9676,225.7939,0.0688,5.2259,70.3939,431.8179,502.543,1232.3428,19.0569,432.0799,705.4787,1,13,64.0,512.0,303.0,44.0,209.0,3.203125,205.0,,,0.0625,4.0,,,,,,,,,,13
job_591150,0.0,0,64,512,4,3.0469,3,1.0,8458.4961,12622.1583,7183.9056,30096,1,8927.1653,0.2344,-0.0072,0.1074,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0716,0.0,0.0,1.0,0.0,0.0087,0.375,0.2344,15.6031,53.179,628.9873,26.7,857.9676,228.9803,0.0787,5.1752,73.5778,416.5122,490.4141,1429.1938,19.0701,416.7571,914.4966,1,14,60.0,480.0,334.0,33.0,146.0,2.716666666666667,163.0,0.0072,9.1814,0.03333333333333333,2.0,,,,,,,,,,14
job_591150,0.0,0,64,512,4,4.0,4,1.0,9658.8066,12490.4551,8288.1246,29820,1,8982.0547,0.3262,0.0033,0.186,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0761,0.0,0.0,1.0,0.0,0.0122,0.4688,0.3262,15.4732,53.0505,626.9476,26.9,857.9676,231.02,0.0661,5.096,76.3654,444.7385,521.4854,1171.3137,18.8645,445.0534,625.8348,1,15,63.0,504.0,225.0,54.0,278.0,4.349206349206349,274.0,,,0.06349206349206349,4.0,,,,,,,5.8824,0.015873015873015872,1.0,15
job_591150,0.0,0,64,512,5,5.0,5,1.0,7392.418,16034.6364,6113.5247,29621,1,9341.5481,0.1289,-0.0013,0.1187,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0553,0.0,0.0,1.0,0.0,0.0095,0.2812,0.1289,15.7165,53.3323,624.8472,27.2,857.9676,233.1204,0.0635,4.8164,68.4974,418.1021,486.9176,2531.0981,13.3106,418.3564,2026.0146,1,16,,,,,,,,0.0074,9.3875,,,,,,,,,,,,16
1 log_file async/discard_rate async/discarded_count async/effective_batch_groups async/effective_batch_samples async/staleness_max async/staleness_mean async/staleness_min async/staleness_ratio generate/avg_num_tokens generate/avg_tokens_non_zero_rewards generate/avg_tokens_zero_rewards generate/max_num_tokens generate/min_num_tokens generate/std_num_tokens loss/avg_final_rewards loss/avg_raw_advantages loss/avg_raw_advantages_abs policy/final_loss policy/log_ratio_abs_max policy/log_ratio_abs_mean policy/log_ratio_abs_p99 policy/log_ratio_abs_pos00 policy/log_ratio_abs_pos10 policy/log_ratio_abs_pos20 policy/log_ratio_abs_pos30 policy/log_ratio_abs_pos40 policy/log_ratio_abs_pos50 policy/log_ratio_abs_pos60 policy/log_ratio_abs_pos70 policy/log_ratio_abs_pos80 policy/log_ratio_abs_pos90 policy/n_tokens_dp_gt_10pct policy/n_tokens_dp_gt_1pct policy/n_tokens_dp_gt_50pct policy/policy_entropy policy/policy_loss policy/policy_lr policy/policy_update_steps policy/ppo_clip_ratio policy/raw_grad_norm reward/avg_pass_at_8 reward/avg_raw_reward system/process_rss_gb system/process_vms_gb system/ram_available_gb system/ram_percent system/ram_total_gb system/ram_used_gb timing/compute_advantages_and_returns timing/convert_to_training_input timing/fwd_logprobs_values_reward timing/policy_train timing/run_training timing/step timing/sync_weights timing/train_critic_and_policy timing/wait_for_generation_buffer trainer/epoch trainer/global_step batch_errors/total_batches batch_errors/total_instances batch_errors/total_successful batch_errors/total_failed batch_errors/total_masked batch_errors/avg_ContextLengthExceededError batch_errors/total_ContextLengthExceededError timing/cleanup_old_checkpoints timing/save_checkpoints batch_errors/avg_DaytonaError batch_errors/total_DaytonaError batch_errors/avg_VerifierTimeoutError batch_errors/total_VerifierTimeoutError batch_errors/avg_InvalidChatHistory batch_errors/total_InvalidChatHistory batch_errors/avg_AgentTimeoutError batch_errors/total_AgentTimeoutError timing/save_hf_model batch_errors/avg_RuntimeError batch_errors/total_RuntimeError global_step
2 job_591150 0.0 0 64 512 0 0.0 0 0.0 8795.541 7557.8707 9821.0393 28234 1862 5511.4955 0.4531 0.0154 0.1383 -0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1147 -0.0 0.0 1.0 0.0 0.0154 0.6094 0.4531 12.2192 50.7338 643.6898 25.0 857.9676 214.2778 0.1433 4.903 67.5598 331.9725 399.8923 2717.3975 19.1362 332.1889 2293.4656 0 1 127.0 1016.0 978.0 16.0 38.0 0.31496062992125984 40.0 1
3 job_591150 0.0 0 64 512 1 0.8906 0 0.8906 10265.4648 9975.6566 10448.2102 30188 1 7251.4743 0.3867 0.0046 0.1482 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1058 0.0 0.0 1.0 0.0 0.0194 0.5312 0.3867 12.8623 51.2017 641.1227 25.3 857.9676 216.8448 0.0832 5.4115 69.2781 417.5714 487.2024 1580.8868 19.4255 417.8407 1068.8446 0 2 61.0 488.0 289.0 42.0 199.0 3.278688524590164 200.0 10.894 19.6772 0.04918032786885246 3.0 0.03278688524590164 2.0 0.03278688524590164 2.0 2
4 job_591150 0.0 0 64 512 2 1.7188 1 1.0 6598.4668 9521.9474 6088.8693 29419 1 7508.721 0.1484 0.014 0.1069 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0709 0.0 0.0 1.0 0.0 0.0109 0.2969 0.1484 12.9957 51.341 637.1793 25.7 857.9676 220.7883 0.0935 4.8247 53.3349 340.9789 394.6829 675.1796 19.7755 341.254 255.893 0 3 60.0 480.0 411.0 22.0 69.0 1.05 63.0 0.016666666666666666 1.0 0.016666666666666666 1.0 3
5 job_591150 0.0 0 64 512 3 2.4219 1 1.0 9999.6543 9601.1462 10199.4927 29478 1 7901.9526 0.334 0.0083 0.0995 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1012 0.0 0.0 1.0 0.0 0.0119 0.4531 0.334 13.3472 51.7056 629.7285 26.6 857.9676 228.2391 0.0901 5.533 67.9116 401.1539 469.4069 1064.6399 19.7968 401.4046 569.8905 0 4 65.0 520.0 384.0 31.0 136.0 2.0153846153846153 131.0 0.0057 12.0885 0.07692307692307693 5.0 4
6 job_591150 0.0 0 64 512 4 3.0625 2 1.0 9386.873 11132.7622 8710.2818 29967 1 8316.5867 0.2793 -0.0005 0.1511 -0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0843 -0.0 0.0 1.0 0.0 0.0126 0.4375 0.2793 13.3903 51.7529 626.9168 26.9 857.9676 231.0508 0.0708 5.2362 79.0552 433.729 513.1297 1514.2234 19.1602 434.0033 976.6598 0 5 62.0 496.0 384.0 32.0 112.0 1.9193548387096775 119.0 6.6576 5
7 job_591150 0.0 0 64 512 4 3.7656 3 1.0 10051.041 12819.4872 8837.9017 29419 1 8180.7526 0.3047 0.0255 0.2105 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0891 0.0 0.0 1.0 0.0 0.0202 0.5469 0.3047 13.8707 51.8625 639.0039 25.5 857.9676 218.9637 0.077 5.2138 69.1587 408.4466 477.9141 961.7158 18.673 408.6779 459.8809 0 6 63.0 504.0 316.0 42.0 187.0 2.9365079365079363 185.0 0.0083 9.9275 0.031746031746031744 2.0 0.015873015873015872 1.0 6
8 job_591150 0.0 0 64 512 5 4.7812 4 1.0 8289.875 10832.8 7553.2594 31013 1 8640.4314 0.2246 -0.0009 0.0888 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0742 0.0 0.0 1.0 0.0 0.0102 0.3281 0.2246 13.6701 51.6724 636.2298 25.8 857.9676 221.7378 0.0697 5.0448 66.0801 402.6137 469.0069 1264.3273 18.9369 402.8567 771.3001 0 7 60.0 480.0 268.0 53.0 211.0 3.45 207.0 0.03333333333333333 2.0 0.06666666666666667 4.0 0.016666666666666666 1.0 7
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