This repository contains the Qwen3-4B Biology GRPO batch-size-32 run. The repository name uses the project GRPO-TR naming convention, but the actual training method for this checkpoint is GRPO.

The repository root contains the final checkpoint, global_step_100. The best validation checkpoint was global_step_90, but that step does not contain an actor model checkpoint in the local run directory, so only the final actor checkpoint can be exported. checkpoints/last/ also contains the same final checkpoint.

Performance

Dataset	Method	Base model	Train batch size	Uploaded checkpoint	Uploaded val mean@16	Best val mean@16	Best checkpoint	Best actor available
Biology	GRPO	Qwen3-4B	32	100	42.62%	46.75%	90	no

Validation Mean@16

step	val_mean16	percent
10	0.326250000000	32.62%
20	0.357500000000	35.75%
30	0.382500000000	38.25%
40	0.353750000000	35.38%
50	0.418750000000	41.88%
60	0.382500000000	38.25%
70	0.395000000000	39.50%
80	0.416250000000	41.62%
90	0.467500000000	46.75%
100	0.426250000000	42.62%

Detailed Training Hyperparameters

Section	Parameter	Value	Source
Run identity	`Base model`	`Qwen/Qwen3-4B`	queue/script override
Run identity	`Dataset`	`Biology / SciKnowEval biology`	run_qwen3_generalization.sh
Run identity	`Method`	`GRPO`	run_qwen3_generalization.sh
Run identity	`Config`	`baseline_grpo`	run_qwen3_generalization.sh
Run identity	`Experiment`	`qwen3gen-biology-GRPO-Qwen-Qwen3-4B-mbs8-train32-rollout8-lr1e-6-vllm0.8`	run_qwen3_generalization.sh
Run identity	`W&B run`	`run-20260702_102415-94h2xs6b`	wandb
Data	`Train file`	`datasets/sciknoweval/biology/train.parquet`	script override
Data	`Validation file`	`datasets/sciknoweval/biology/test.parquet`	script override
Data	`Train batch size`	`32`	queue/script override
Data	`Train max samples`	`3200`	queue/script override
Data	`Prompt key`	`prompt`	legacy_data.yaml default
Data	`Reward key`	`data_source`	legacy_data.yaml default
Data	`Shuffle train data`	`True`	user.yaml / legacy_data.yaml
Data	`Validation shuffle`	`False`	legacy_data.yaml default
Data	`Filter overlong prompts`	`True`	user.yaml
Data	`Prompt truncation`	`error`	legacy_data.yaml default
Data	`enable_thinking`	`false`	script override
Schedule	`Total training steps`	`100`	queue/script override
Schedule	`Total epochs`	`30`	ppo_trainer/user.yaml default
Schedule	`Validation before train`	`False`	queue/script override
Schedule	`Save frequency`	`10`	queue/script override
Schedule	`Validation frequency`	`10`	queue/script override
Sequence	`Max prompt length`	`2048`	queue/script override
Sequence	`Max response length`	`8192`	queue/script override
Sequence	`Max model length`	`10240`	queue/script override
Sequence	`Actor max token length per GPU`	`10240`	queue/script override
Rollout	`Rollout engine`	`vllm`	user.yaml
Rollout	`Rollout dtype`	`bfloat16`	rollout.yaml default
Rollout	`Train rollout n`	`8`	queue/script override
Rollout	`Train rollout temperature`	`1.0`	script override
Rollout	`Train rollout top_p`	`1.0`	script override
Rollout	`Train rollout do_sample`	`True`	rollout.yaml default
Rollout	`Calculate rollout log probs`	`True`	baseline_grpo.yaml / script override
Rollout	`Max num batched tokens`	`10240`	queue/script override
Rollout	`vLLM GPU memory utilization`	`0.8`	queue/script override
Rollout	`Tensor model parallel size`	`2`	rollout.yaml default
Rollout	`Free cache engine`	`True`	rollout.yaml default
Validation	`Validation rollout n`	`16`	queue/script override
Validation	`Validation temperature`	`0.6`	queue/script override
Validation	`Validation top_p`	`0.95`	queue/script override
Validation	`Validation do_sample`	`True`	queue/script override
Optimization	`Optimizer`	`AdamW`	fsdp optimizer config
Optimization	`Learning rate`	`1e-6`	GRPO method override
Optimization	`LR scheduler`	`constant`	W&B config
Optimization	`LR warmup steps`	`10`	script override
Optimization	`Weight decay`	`0.01`	script override
Optimization	`Betas`	`(0.9, 0.999)`	W&B config
Optimization	`Gradient clip`	`1.0`	script override
PPO/GRPO	`Policy loss mode`	`vanilla`	method override
PPO/GRPO	`Advantage estimator`	`grpo`	baseline_grpo.yaml
PPO/GRPO	`Normalize GRPO advantages by std`	`False`	baseline_grpo.yaml / script override
PPO/GRPO	`PPO epochs`	`1`	W&B config
PPO/GRPO	`PPO mini batch size`	`8`	queue/script override
PPO/GRPO	`PPO micro batch size per GPU`	`1`	user.yaml
PPO/GRPO	`Clip ratio low`	`0.2`	script override
PPO/GRPO	`Clip ratio high`	`0.28`	script override
PPO/GRPO	`Gamma`	`1.0`	ppo_trainer.yaml default
PPO/GRPO	`Lambda`	`1.0`	ppo_trainer.yaml default
PPO/GRPO	`Actor KL loss coef`	`0.0`	method override
PPO/GRPO	`Use KL in reward`	`False`	ppo_trainer/user.yaml
Rollout correction	`Importance sampling mode`	`token`	script override
Rollout correction	`IS threshold`	`2.0`	script override
FSDP/System	`Actor strategy`	`fsdp`	dp_actor.yaml
FSDP/System	`FSDP dtype`	`bfloat16`	W&B config
FSDP/System	`FSDP model dtype`	`fp32`	W&B config
FSDP/System	`Use torch compile`	`True`	W&B config
FSDP/System	`GPUs per node`	`8`	queue/script override
FSDP/System	`Nodes`	`1`	user.yaml
FSDP/System	`GPU type`	`NVIDIA H200`	wandb-metadata
Checkpoint/Logging	`Checkpoint root`	`checkpoints/datasets/sciknoweval/biology`	script override
Checkpoint/Logging	`Latest checkpointed iteration`	`100`	latest_checkpointed_iteration.txt
Checkpoint/Logging	`Max actor checkpoints to keep`	`1`	user.yaml
Checkpoint/Logging	`Logger`	`console, wandb`	ppo_trainer.yaml
Checkpoint/Logging	`W&B entity`	`seongryongjung-chung-ang-university`	environment
Checkpoint/Logging	`W&B project`	`SDPO-root`	user.yaml project_name
Checkpoint/Logging	`W&B group`	`QWEN3-GRPO-generalization`	method override

Raw result and artifact files:

results/validation_mean16.csv
results/training_scores.csv
results/hyperparameters.csv
results/training_score.png
results/training_score.svg
artifacts/config.yaml
artifacts/wandb-summary.json
artifacts/wandb-metadata.json
artifacts/output.log
artifacts/queue.log

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

repo_id = "SeongryongJung/Qwen3-4B-Biology-GRPO-TR"
tokenizer = AutoTokenizer.from_pretrained(repo_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    repo_id,
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True,
)

Source

Checkpoint: checkpoints/datasets/sciknoweval/biology/qwen3gen-biology-GRPO-Qwen-Qwen3-4B-mbs8-train32-rollout8-lr1e-6-vllm0.8
Uploaded actor checkpoint: global_step_100/actor
Best validation step: global_step_90; actor checkpoint unavailable locally
W&B run: run-20260702_102415-94h2xs6b
Queue log: artifacts/queue.log