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Model: Linyuana/qwen3-0.6b-grpo-math-reasoning Source: Original Platform
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README.md
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---
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license: apache-2.0
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base_model: Qwen/Qwen3-0.6B-Base
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tags:
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- reasoning
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- math
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- grpo
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- reinforcement-learning
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- rlvr
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- qwen3
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datasets:
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- gsm8k
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- math
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language:
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- en
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pipeline_tag: text-generation
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---
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# Qwen3-0.6B GRPO Math Reasoning
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Qwen3-0.6B-Base fine-tuned with SFT cold-start followed by **GRPO** (Group Relative Policy Optimization) with a verifiable rule-based reward, on GSM8K + MATH. Part of a reproduction study on the DeepSeek-R1 recipe comparing GRPO / PPO / DPO under matched initialization — full writeup, ablations, and failure-mode analysis (including a PPO training collapse and fix) here: [Linyuan30/llm-rl-reasoning](https://github.com/Linyuan30/llm-rl-reasoning).
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## Training Recipe
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```
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Qwen3-0.6B-Base --SFT (cold start)--> unified <think>/<answer> format --GRPO--> this checkpoint
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```
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- **Reward**: purely rule-based — regex-extract the `<answer>` tag, normalize, compare to ground truth. No reward model.
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- **Algorithm**: GRPO, group-relative advantage (no critic/value model).
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- **Framework**: [veRL](https://github.com/volcengine/verl) v0.4.0 + vLLM for rollout.
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- Checkpoint corresponds to `global_step_116` of `grpo_qwen3_0.6b` (best result in the sweep).
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## Results
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Evaluated on 500 held-out samples/dataset, temperature=0.8, top_p=0.95, 8 samples/question, same rule-reward scorer used at both train and eval time.
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| Method | GSM8K pass@1 | GSM8K pass@8 | MATH pass@1 | MATH pass@8 |
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| --- | ---: | ---: | ---: | ---: |
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| Base (no post-training) | 4.8 | 30.6 | 3.7 | 24.8 |
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| SFT only | 38.9 | 78.4 | 29.0 | 66.4 |
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| **SFT + GRPO (this model)** | **67.7** | **85.0** | **48.8** | **79.2** |
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GRPO clearly outperformed PPO (42.6 pass@1) and DPO (39.0 pass@1) under the same base model, data, and reward — see [docs/grpo_analysis.md](https://github.com/Linyuan30/llm-rl-reasoning/blob/master/docs/grpo_analysis.md) for the group-size ablation (n=4/8/16) and why GRPO's critic-free advantage estimation avoided the instability PPO ran into.
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "Linyuana/qwen3-0.6b-grpo-math-reasoning"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")
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prompt = "Natalia sold clips to 48 of her friends in April, and then she sold half as many clips in May. How many clips did Natalia sell altogether in April and May?"
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messages = [{"role": "user", "content": prompt}]
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inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
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output = model.generate(inputs, max_new_tokens=512, temperature=0.8, top_p=0.95, do_sample=True)
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print(tokenizer.decode(output[0], skip_special_tokens=True))
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```
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The model outputs a `<think>...</think><answer>...</answer>` format; extract the final answer from within the `<answer>` tag.
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## Limitations
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- 0.6B scale — solid gains from RL, but absolute accuracy is well below what larger models achieve on MATH.
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- `strict_format_rate` (exact `<think>/<answer>` tag closure) is lower than expected across all training stages despite `has_answer_rate` >94%; this is a known open issue in the format-matching regex, not a correctness issue — see [Open Question in the repo](https://github.com/Linyuan30/llm-rl-reasoning#open-question).
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- Trained/evaluated only on GSM8K and MATH; not tested on other reasoning benchmarks.
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## Citation / Acknowledgements
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Built on [Qwen3](https://huggingface.co/Qwen), trained with [veRL](https://github.com/volcengine/verl) and [vLLM](https://github.com/vllm-project/vllm), following the [DeepSeek-R1](https://arxiv.org/abs/2501.12948) RLVR recipe.
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||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# 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>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\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\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set content = message.content %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is defined and message.reasoning_content is not none %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '</think>' in message.content %}\n {%- set content = message.content.split('</think>')[-1].lstrip('\\n') %}\n {%- set reasoning_content = message.content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '<think>\\n\\n</think>\\n\\n' }}\n {%- endif %}\n{%- endif %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|endoftext|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user