90ed71fcdc4a3ae9e8413fb0a00d1df63e29cbd8
Model: SeongryongJung/Qwen3-8B-Physics-GRPO-TR Source: Original Platform
license, library_name, pipeline_tag, tags, base_model
| license | library_name | pipeline_tag | tags | base_model | ||||
|---|---|---|---|---|---|---|---|---|
| apache-2.0 | transformers | text-generation |
|
Qwen/Qwen3-8B |
Qwen3-8B-Physics-GRPO-TR
This repository contains the Qwen3-8B physics GRPO batch-size-32 run. The repository name uses the project GRPO-TR naming convention, but the actual training method for this checkpoint is GRPO.
The repository root contains the best validation checkpoint, selected by validation mean@16. checkpoints/last/ contains the final checkpoint.
Performance
| Dataset | Method | Base model | Train batch size | Best val mean@16 | Best checkpoint | Final val mean@16 | Final checkpoint |
|---|---|---|---|---|---|---|---|
| Physics / SciKnowEval physics | GRPO | Qwen3-8B | 32 | 72.97% | 100 | 72.97% | 100 |
Validation Mean@16
| step | val_mean16 | percent |
|---|---|---|
| 10 | 0.583593750000 | 58.36% |
| 20 | 0.599218750000 | 59.92% |
| 30 | 0.604687500000 | 60.47% |
| 40 | 0.621093750000 | 62.11% |
| 50 | 0.653906250000 | 65.39% |
| 60 | 0.671875000000 | 67.19% |
| 70 | 0.681250000000 | 68.12% |
| 80 | 0.712500000000 | 71.25% |
| 90 | 0.722656250000 | 72.27% |
| 100 | 0.729687500000 | 72.97% |
Detailed Training Hyperparameters
| Section | Parameter | Value | Source |
|---|---|---|---|
| Run identity | Base model |
Qwen/Qwen3-8B |
queue/script override |
| Run identity | Dataset |
Physics / SciKnowEval physics |
run_qwen3_generalization.sh |
| Run identity | Method |
GRPO |
run_qwen3_generalization.sh |
| Run identity | Config |
baseline_grpo |
run_qwen3_generalization.sh |
| Run identity | Experiment |
qwen3gen-physics-GRPO-Qwen-Qwen3-8B-mbs8-train32-rollout8-lr1e-6-vllm0.8 |
run_qwen3_generalization.sh |
| Run identity | W&B run |
run-20260703_032040-6ig3l55l |
wandb |
| Data | Train file |
datasets/sciknoweval/physics/train.parquet |
script override |
| Data | Validation file |
datasets/sciknoweval/physics/test.parquet |
script override |
| Data | Train batch size |
32 |
queue/script override |
| Data | Train max samples |
3200 |
queue/script override |
| Schedule | Total training steps |
100 |
queue/script override |
| Schedule | Validation before train |
False |
queue/script override |
| Schedule | Save frequency |
10 |
queue/script override |
| Schedule | Validation frequency |
10 |
queue/script override |
| Sequence | Max prompt length |
2048 |
queue/script override |
| Sequence | Max response length |
8192 |
queue/script override |
| Sequence | Max model length |
10240 |
queue/script override |
| Rollout | Train rollout n |
8 |
queue/script override |
| Rollout | Validation rollout n |
16 |
queue/script override |
| Rollout | vLLM GPU memory utilization |
0.8 |
queue/script override |
| Optimization | Learning rate |
1e-6 |
GRPO method override |
| Optimization | Weight decay |
0.01 |
script override |
| PPO/GRPO | PPO mini batch size |
8 |
queue/script override |
| PPO/GRPO | Normalize GRPO advantages by std |
False |
baseline_grpo.yaml / script override |
| Rollout correction | Importance sampling mode |
token |
script override |
| Rollout correction | IS threshold |
2.0 |
script override |
| Checkpoint/Logging | Checkpoint root |
checkpoints/datasets/sciknoweval/physics/qwen3gen-physics-GRPO-Qwen-Qwen3-8B-mbs8-train32-rollout8-lr1e-6-vllm0.8 |
script override |
| Checkpoint/Logging | Latest checkpointed iteration |
100 |
latest_checkpointed_iteration.txt |
| Checkpoint/Logging | External actor archive |
checkpoints/datasets/sciknoweval/physics/qwen3gen-physics-GRPO-Qwen-Qwen3-8B-mbs8-train32-rollout8-lr1e-6-vllm0.8/_actor_archive |
preserve_actor_checkpoints.py |
| Checkpoint/Logging | Logger |
console, wandb |
ppo_trainer.yaml |
| PPO/GRPO | Policy loss mode |
vanilla |
method override |
| PPO/GRPO | Actor KL loss coef |
0.0 |
method override |
Raw result and artifact files:
results/validation_mean16.csvresults/training_scores.csvresults/hyperparameters.csvresults/training_score.pngresults/training_score.svgartifacts/output.logartifacts/queue.log
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
repo_id = "SeongryongJung/Qwen3-8B-Physics-GRPO-TR"
tokenizer = AutoTokenizer.from_pretrained(repo_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
repo_id,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True,
)
Source
- Checkpoint:
checkpoints/datasets/sciknoweval/physics/qwen3gen-physics-GRPO-Qwen-Qwen3-8B-mbs8-train32-rollout8-lr1e-6-vllm0.8 - Root actor checkpoint:
checkpoints/datasets/sciknoweval/physics/qwen3gen-physics-GRPO-Qwen-Qwen3-8B-mbs8-train32-rollout8-lr1e-6-vllm0.8/global_step_100/actor - Last actor checkpoint:
checkpoints/datasets/sciknoweval/physics/qwen3gen-physics-GRPO-Qwen-Qwen3-8B-mbs8-train32-rollout8-lr1e-6-vllm0.8/global_step_100/actor - W&B run:
run-20260703_032040-6ig3l55l - Queue log:
artifacts/queue.log
Description
Languages
Jinja
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