This repository contains the final merged Hugging Face checkpoint from global_step_100. The training checkpoint was saved from FSDP shards and merged to safetensors for this upload.

Training Method

Policy loss mode: rlrt.
Self-distillation uses reprompt feedback and token reweighting.
Token reweighting: lambda 0.5, eps_w 1.0, decay disabled.
Advantage estimator remains grpo in the trainer config.
Reward function: local math compute_score reward manager.
Fine-tuning type: full-parameter FSDP training, not LoRA.

Training Hyperparameters

Field	Value
Base model	`Qwen/Qwen3-4B-Base`
Train file	`/home1/irteam/SDPO/self-distillation-analysis/data/math/train.parquet`
Validation file	`/home1/irteam/SDPO/self-distillation-analysis/data/math/evaluation/aime24.parquet`
Train max samples	25600
Train batch size	256
Rollouts per prompt	8
PPO mini batch size	128
PPO micro batch size per GPU	1
Optimizer	AdamW
Learning rate	1e-06
Weight decay	0.01
LR warmup steps	10
Total training steps	100
Save frequency	every 10 steps
Validation frequency	every 10 steps
Max prompt length	2048
Max response length	20480
Rollout backend	vllm
Rollout temperature	1
Rollout top_p	1
vLLM GPU memory utilization	0.75
Actor strategy	fsdp
Dtype	bfloat16
Advantage estimator	grpo
Gamma / Lambda	1 / 1
KL loss enabled	False
KL loss coefficient	0.001
Checkpoint uploaded	`math-RLRT-Qwen3-4B-Base-128-train256-rollout8-lr1e-6-vllm0.75-modelQwen-Qwen3-4B-Base/global_step_100`
W&B run id	`exyyiq0s`

Training Score

The plot below shows critic/score/mean logged during training.

CSV data is included in training_score.csv.

Metric	Value
Final training step	100
Final `critic/score/mean`	0.462402
Final `val-core/math_dapo/acc/mean@1`	0.3

Intended Use

This model is intended for internal research and analysis of math-focused RL fine-tuning methods. It has not been broadly safety evaluated for production use.

Limitations

The model was trained for 100 optimization steps on a local math dataset split. Reported scores are training-time reward/validation metrics from the same experiment setup and should not be treated as broad benchmark results.

SeongryongJung/Qwen-4b-base-RLRT

Qwen3-4B-Base RLRT

Training Method

Training Hyperparameters

Training Score

Intended Use

Limitations

SeongryongJung/
Qwen-4b-base-RLRT