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Model: jaygala24/Qwen2.5-3B-ReMax-math-reasoning
Source: Original Platform

README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model: Qwen/Qwen2.5-3B
+tags:
+- reinforcement-learning
+- remax
+- math-reasoning
+- pipelinerl
+datasets:
+- gsm8k_train
+- math_train
+pipeline_tag: text-generation
+---
+
+# Qwen2.5-3B-ReMax-math-reasoning
+
+This model is a fine-tuned version of [Qwen2.5-3B](https://huggingface.co/Qwen/Qwen2.5-3B) using **ReMax without KL penalty** for mathematical reasoning.
+
+Trained with [PipelineRL](https://github.com/ServiceNow/PipelineRL).
+
+## Training Details
+
+### Datasets
+
+| Split | Datasets |
+|-------|----------|
+| Train | `gsm8k_train`, `math_train` |
+| Test  | `gsm8k_test`, `math_500` |
+
+### RL Algorithm
+
+| Parameter | Value |
+|-----------|-------|
+| Algorithm | ReMax |
+| Advantage Baseline | Greedy-decoded response reward |
+| Extra Inference | 1 deterministic rollout per prompt |
+| Group Structure | Not required |
+| Policy Loss | `ppo` |
+| KL Coefficient | `0.0` |
+| Epsilon (clip) | `0.2` |
+| Discount Factor (`gamma`) | `1.0` |
+| Divide Advantage by Std | `False` |
+| Filter Zero Advantage Groups | `False` |
+| Rollouts per Problem | `16` |
+
+ReMax uses a greedy-decoded response's reward as the baseline for advantages.
+
+### Training Hyperparameters
+
+| Parameter | Value |
+|-----------|-------|
+| Base Model | `Qwen/Qwen2.5-3B` |
+| Learning Rate | `1e-06` |
+| LR Scheduler | `cosine` |
+| Warmup Steps | `25` |
+| Max Training Steps | `1500` |
+| Micro Batch Size | `2` |
+| Gradient Accumulation | `128` |
+| Effective Batch Size | `256` |
+| Sequence Length | `8192` |
+| Gradient Clipping | `0.3` |
+| Weight Decay | `0.01` |
+| Optimizer | `adamw_torch` |
+| Precision | `bf16` |
+| DeepSpeed | ZeRO Stage 3 |
+
+## Evaluation Results
+
+Pass@k on math reasoning benchmarks (N=32 samples per problem, temperature=1.0):
+
+| Dataset | pass@1 | pass@2 | pass@4 | pass@8 | pass@16 | pass@32 |
+| --- | ---: | ---: | ---: | ---: | ---: | ---: |
+| GSM8K (test) | 85.99 | 90.50 | 93.34 | 95.29 | 96.64 | 97.50 |
+| MATH-500 | 67.36 | 74.99 | 81.23 | 85.92 | 89.09 | 91.20 |
+| **Overall** | **80.87** | **86.24** | **90.01** | **92.71** | **94.56** | **95.77** |
+
+*GSM8K test: 1319 problems · MATH-500: 500 problems · Overall: 1819 problems (overall weighted by problem count).*
+
+## Training Curves
+
+![Training Metrics](training_metrics.png)
+
+## W&B Run
+
+Full training logs: [https://wandb.ai/jaygala24-team/rl-post-training/runs/qwen2.5_3b_remax_3a1f_4xh100_214753_finetune_1c7d72aa](https://wandb.ai/jaygala24-team/rl-post-training/runs/qwen2.5_3b_remax_3a1f_4xh100_214753_finetune_1c7d72aa)
+
+## Usage
+
+### Transformers
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("jaygala24/Qwen2.5-3B-ReMax-math-reasoning", revision="step-0200")  # optional branch, e.g. "step-0400"
+tokenizer = AutoTokenizer.from_pretrained("jaygala24/Qwen2.5-3B-ReMax-math-reasoning", revision="step-0200")
+
+prompt = "Please reason step by step, and put your final answer within \\boxed{}.\n\nWhat is the sum of 123 and 456?"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=4096, temperature=0.7)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+### vLLM
+
+```python
+from vllm import LLM, SamplingParams
+
+llm = LLM(model="jaygala24/Qwen2.5-3B-ReMax-math-reasoning", revision="step-0200")  # optional branch, e.g. "step-0400"
+sampling_params = SamplingParams(temperature=0.7, max_tokens=4096)
+
+prompt = "Please reason step by step, and put your final answer within \boxed{}.
+
+What is the sum of 123 and 456?"
+outputs = llm.generate([prompt], sampling_params)
+print(outputs[0].outputs[0].text)
+```
+
+## Framework
+
+- [PipelineRL](https://github.com/ServiceNow/PipelineRL)
+- [Transformers](https://github.com/huggingface/transformers)
+- [DeepSpeed](https://github.com/microsoft/DeepSpeed) (ZeRO Stage 3)