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jaygala24/Qwen2.5-0.5B-GRPO-math-reasoning

sourceHugging Faceapache-2.0updated 5mo agoView on Hugging Face

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Model Card

Qwen2.5-0.5B-GRPO-math-reasoning

This model is a fine-tuned version of Qwen2.5-0.5B using GRPO (Group Relative Policy Optimization) without KL penalty for mathematical reasoning.

Trained with PipelineRL.

Training Details

Datasets

Split	Datasets
Train	`gsm8k_train`, `math_train`
Test	`gsm8k_test`, `math_500`

RL Algorithm

Parameter	Value
Algorithm	GRPO (Group Relative Policy Optimization)
Advantage Baseline	Group mean reward
Extra Inference	None
Group Structure	Required
Policy Loss	`ppo`
KL Coefficient	`0.0`
Epsilon (clip)	`0.02`
Discount Factor (`gamma`)	`1.0`
Divide Advantage by Std	`False`
Filter Zero Advantage Groups	`False`
Rollouts per Problem	`16`

GRPO uses the group mean reward as the baseline for relative advantages.

Training Hyperparameters

Parameter	Value
Base Model	`Qwen/Qwen2.5-0.5B`
Learning Rate	`1e-06`
LR Scheduler	`cosine`
Warmup Steps	`25`
Max Training Steps	`1500`
Micro Batch Size	`8`
Gradient Accumulation	`32`
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)	51.77	62.24	71.12	78.64	84.85	89.76
MATH-500	31.18	40.46	49.63	58.37	66.26	73.00
Overall	46.11	56.25	65.22	73.07	79.74	85.16

GSM8K test: 1319 problems · MATH-500: 500 problems · Overall: 1819 problems (overall weighted by problem count).

Training Curves

[image]

W&B Run

Full training logs: https://wandb.ai/jaygala24-team/rl-post-training/runs/qwen2.5_0.5b_grpo_no_kl_3a1f_4xh100_202888_finetune_cd3a050a

Usage

Transformers

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("jaygala24/Qwen2.5-0.5B-GRPO-math-reasoning", revision="step-0200")  # optional branch, e.g. "step-0400"
tokenizer = AutoTokenizer.from_pretrained("jaygala24/Qwen2.5-0.5B-GRPO-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/Qwen2.5-0.5B-GRPO-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
—Transformers
—DeepSpeed (ZeRO Stage 3)