library_name, license, base_model, tags, datasets, pipeline_tag

library_name	license	base_model	tags	datasets	pipeline_tag
transformers	apache-2.0	Qwen/Qwen2.5-1.5B	reinforcement-learning rloo math-reasoning pipelinerl	gsm8k_train math_train	text-generation

Qwen2.5-1.5B-RLOO-math-reasoning

This model is a fine-tuned version of Qwen2.5-1.5B using RLOO (REINFORCE Leave-One-Out) 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	RLOO (REINFORCE Leave-One-Out)
Advantage Baseline	Leave-one-out mean reward over the group
Extra Inference	None
Group Structure	Required
Policy Loss	reinforce
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

RLOO uses the leave-one-out mean of the other responses in the group as the baseline, trained with a REINFORCE-style policy loss.

Training Hyperparameters

Parameter	Value
Base Model	Qwen/Qwen2.5-1.5B
Learning Rate	1e-06
LR Scheduler	cosine
Warmup Steps	25
Max Training Steps	1500
Micro Batch Size	4
Gradient Accumulation	64
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)	78.44	85.37	89.97	92.93	94.80	96.06
MATH-500	60.14	68.63	75.63	81.47	86.24	89.80
Overall	73.41	80.77	86.03	89.78	92.45	94.34

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

Training Curves

W&B Run

Full training logs: https://wandb.ai/jaygala24-team/rl-post-training/runs/qwen2.5_1.5b_rloo_no_kl_3a1f_4xh100_236657_finetune_27b80841

Usage

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("jaygala24/Qwen2.5-1.5B-RLOO-math-reasoning", revision="step-0200")  # optional branch, e.g. "step-0400"
tokenizer = AutoTokenizer.from_pretrained("jaygala24/Qwen2.5-1.5B-RLOO-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

from vllm import LLM, SamplingParams

llm = LLM(model="jaygala24/Qwen2.5-1.5B-RLOO-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)