license, base_model, library_name, pipeline_tag, tags

license	base_model	library_name	pipeline_tag	tags
apache-2.0	Qwen/Qwen3-4B	transformers	text-generation	qwen3 chemistry reinforcement-learning verl sciknoweval sdpo self-distillation

Qwen3-4B Chemistry SDPO

This repository contains Chemistry fine-tuned Qwen3-4B checkpoints from the local SciKnowEval-style generalization setup.

• Root checkpoint: final global_step_100 merged to Hugging Face safetensors.
• best_avg16/: checkpoint with the highest validation avg@16 during training, merged to Hugging Face safetensors.

Checkpoints

Checkpoint	Source step	Validation avg@16	best@16	maj@16
Root final	100	0.720536	0.731567	0.721367
best_avg16/	20	0.766369	0.822871	0.779486

Training Run

qwen3gen-chemistry-SDPO-Qwen-Qwen3-4B-mbs32-ema0.05-train256-rollout8-lr1e-5-vllm0.8

W&B run: https://wandb.ai/seongryongjung-chung-ang-university/qwen3-generalization-batch256/runs/1qazekk3

Base Model

• Base model: Qwen/Qwen3-4B
• Fine-tuning type: full-parameter FSDP RL training
• Dataset: datasets/sciknoweval/chemistry
• Train split: 1,890 examples
• Validation split: 210 examples

Method

• Method: SDPO
• Config: sdpo
• Policy loss mode: sdpo
• Reward: local SciKnowEval multiple-choice reward checker
• Rollout correction: token-level importance sampling, threshold 2.0

Hyperparameters

Field	Value
Base model	Qwen/Qwen3-4B
Training steps	100
Train batch size	256
Rollouts per prompt	8
Generations per step	2048
PPO mini batch size	32
Learning rate	1e-5
LR warmup steps	10
Weight decay	0.01
Grad clip	1.0
Max prompt length	2048
Max response length	8192
Max model length	10240
Train temperature	1.0
Train top_p	1.0
Validation generations	16
Validation temperature	0.6
Validation top_p	0.95
vLLM GPU memory utilization	0.8
GPUs	8 x NVIDIA H200
Save frequency	every 10 steps
Validation frequency	every 10 steps
Distillation top-k	100
SDPO alpha	0.5
Teacher update rate	0.05
Distillation IS clip	2.0
Max reprompt length	10240

Metrics

CSV files:

• training_score.csv
• validation_metrics.csv

Metric	Value
Final training step	100
Final critic/score/mean	0.845215
Final critic/rewards/mean	0.845215
Final validation avg@16	0.720536
Peak validation avg@16	0.766369
Peak validation step	20

Loading

Root final checkpoint:

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SeongryongJung/Qwen3-4B-Chemistry-SDPO")
tokenizer = AutoTokenizer.from_pretrained("SeongryongJung/Qwen3-4B-Chemistry-SDPO")

Best avg@16 checkpoint:

model = AutoModelForCausalLM.from_pretrained("SeongryongJung/Qwen3-4B-Chemistry-SDPO", subfolder="best_avg16")
tokenizer = AutoTokenizer.from_pretrained("SeongryongJung/Qwen3-4B-Chemistry-SDPO", subfolder="best_avg16")

Intended Use

This model is intended for research on RL fine-tuning and self-distillation behavior on science/generalization tasks. It has not been broadly safety evaluated for production use.

Limitations

The reported scores are training-time and validation-time metrics from the local experimental setup. They should not be interpreted as broad benchmark results without independent evaluation.