qwen3-4B-refiner-rubric-rl-step50/README.md

library_name, base_model, model_type, license, tags

library_name	base_model	model_type	license	tags
transformers	lihaoxin2020/qwen3-4B-refiner-sft-step-3201	qwen3	apache-2.0	refiner grpo rl qwen3 rubric

qwen3-4B-refiner-rubric-rl-step50

This model is a GRPO-trained checkpoint (step 50) of the Qwen3-4B refiner, fine-tuned with reinforcement learning using per-instance rubric rewards.

• Experiment name: instance-rubric-rl-5e-6-answer_only

Training Details

• Base model: lihaoxin2020/qwen3-4B-refiner-sft-step-3201
• Training method: GRPO (Group Relative Policy Optimization) with DeepSpeed Stage 3
• Refiner mode: answer_only
• Training script: open_instruct/grpo_fast_refiner_rubric.py

Hyperparameters

Parameter	Value
Learning rate	5e-6
LR scheduler	constant
Beta (KL penalty)	0.001
KL estimator	kl3
Advantage normalization	standard
Samples per prompt (rollout)	8
Unique prompts per rollout	32
Mini batches	1
Epochs per batch	1
Per-device train batch size	1
Temperature	1.0
Seed	42
Async mode	true
Adam offload	true
vLLM sync backend	nccl

Sequence Lengths

Parameter	Value
Max token length	8192
Max prompt token length	6144
Response length	1024
Pack length	8192

Reward Configuration

Parameter	Value
Verification reward	10.0
Non-stop penalty	false
Gate judge score with format bonus	true
Apply paper citation reward	true
Paper citation weight	0.2

Dataset

• Training: lihaoxin2020/rl_hard_gpt5_sft (split: train)
• Evaluation: lihaoxin2020/rl_hard_gpt5_sft (16 samples, split: train)

Infrastructure

• DeepSpeed stage: 3
• Learners per node: 1
• vLLM engines: 1
• vLLM tensor parallel size: 1
• vLLM GPU memory utilization: 0.90
• Judge model: Qwen/Qwen3.5-35B-A3B (via vLLM)