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Model: lihaoxin2020/qwen3-4b-refiner-gpt54-instance-rubric-gpt54-grpo-step50
Source: Original Platform

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+license: apache-2.0
+base_model: lihaoxin2020/qwen3-4b-refiner-gpt54-ep2
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+  - grpo
+  - rlhf
+  - refiner
+  - qwen3
+  - open-instruct
+---
+
+# qwen3-4b-refiner-gpt54-instance-rubric-gpt54-grpo-step50
+
+GRPO checkpoint (step 50) of a 4B research-refiner policy.
+
+- **Base model:** [`lihaoxin2020/qwen3-4b-refiner-gpt54-ep2`](https://huggingface.co/lihaoxin2020/qwen3-4b-refiner-gpt54-ep2) (SFT on GPT-5.4 rubric-grounded refiner data)
+- **Algorithm:** GRPO (`grpo_fast_refiner_rubric.py` in [`dr-tulu/rl/open-instruct`](https://github.com/))
+- **Training step:** 50
+- **Task:** Given an agent's reasoning + search-tool output, produce a concise, citation-grounded refined answer.
+
+## Training setup
+
+- **Dataset:** `lihaoxin2020/rl_hard_gpt5_sft_gpt54rubric` (train split) — each row carries per-instance `positive_rubrics` / `negative_rubrics` generated by GPT-5.4.
+- **Reward = LLM rubric judge + paper-citation quality.** For each rollout the judge sees the concatenation of the row's per-instance rubrics and a static V3 rubric set (`Citation Discipline`, `Off-Query Padding`, `Wrong-Entity Confabulation`). Positive rubric scores enter as-is; negative rubrics are flipped (`1 - s`) so higher = better.
+- **Judge:** `Qwen/Qwen3.5-35B-A3B` served via local vLLM.
+- **Citation reward:** enabled, weight 0.2 (paper-citation precision/recall/F1 from the judge over claim-level snippet attributions).
+- **KL:** `kl3` estimator, `beta = 0.001`.
+- **Optimizer:** AdamW (offload off), `lr = 5e-6` constant, `per_device_train_batch_size = 1`, DeepSpeed ZeRO-3, FlashAttn.
+- **Rollouts:** 32 unique prompts × 8 samples per prompt (group size 8), `temperature = 1.0`.
+- **Context:** `max_prompt_token_length = 6144`, `response_length = 1024`, `max_token_length = 8192`, `pack_length = 8192`.
+- **Seed:** 42.
+
+## Refiner output format
+
+The model is trained to emit a single `<answer>...</answer>` block. Every factual claim is wrapped in `<snippet id=ID1,ID2,...>claim</snippet>`, citing only IDs that appear in the raw tool output.
+
+## Intended use
+
+This is a **step-50 intermediate** checkpoint, useful as an early-training baseline for ablations on rubric design, citation weighting, or judge choice. For a final-quality refiner, prefer a later step from the same run or the successor run that drops static rubrics.
+
+## Reproduction
+
+Training script: [`train_dr_tulu_refiner-rubric.sh`](https://github.com/) (repo-internal). Relevant flags:
+
+```
+--verification_reward 10.0
+--non_stop_penalty false
+--refiner_reward_gate_judge_score_with_format_bonus false
+--refiner_reward_apply_paper_citation_reward true
+--refiner_reward_paper_citation_weight 0.2
+--kl_estimator kl3 --beta 0.001
+--num_unique_prompts_rollout 32 --num_samples_per_prompt_rollout 8
+--learning_rate 5e-6 --lr_scheduler_type constant
+--max_prompt_token_length 6144 --response_length 1024 --pack_length 8192
+--temperature 1.0 --seed 42
+--deepspeed_stage 3
+```
+
+## Known notes
+
+- This run was terminated before completion at step ~100 due to a disk-quota issue on the training host; step 50 is a healthy intermediate checkpoint.
+- A later iteration of the training script introduced a `--refiner_reward_instance_rubrics_only` flag that drops the static V3 rubrics from both reward and logging. **This checkpoint was produced before that flag existed**, so both per-instance and static V3 rubrics shaped its reward signal.

added_tokens.json

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+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
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+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
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+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
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+  "<|vision_end|>": 151653,
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+}

chat_template.jinja

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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 %}
+{%- 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" %}
+        {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- 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' }}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
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+    "full_attention",
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+    "full_attention",
+    "full_attention",
+    "full_attention",
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+  "max_position_embeddings": 262144,
+  "max_window_layers": 36,
+  "model_type": "qwen3",
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+  "num_hidden_layers": 36,
+  "num_key_value_heads": 8,
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+  "rope_scaling": null,
+  "rope_theta": 5000000,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "4.57.6",
+  "use_cache": false,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

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+  "do_sample": true,
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+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "4.57.6"
+}

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