Qwen3-4B-Tooluse-RLSD-TR

SeongryongJung/Qwen3-4B-Tooluse-RLSD-TR

Qwen3-4B-Tooluse-RLSD-TR is a text generation model from SeongryongJung. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as apache-2.0.

This repository contains the Qwen3-4B tooluse RLSDTR batch-size-32 run.

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At a glance

Task: Text Generation
Library: transformers
License: apache-2.0
Model type: qwen3
Access: Public
Created: Jul 3, 2026
Updated: Jul 3, 2026
SHA: 44313f10

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Base models

Qwen/Qwen3-4B

Model card

From the Hugging Face model README

Qwen3-4B-Tooluse-RLSD-TR

This repository contains the Qwen3-4B tooluse RLSD_TR batch-size-32 run.

The repository root contains the best validation checkpoint, selected by validation mean@16. checkpoints/last/ contains the final checkpoint.

Performance

Dataset	Method	Base model	Train batch size	Best val mean@16	Best checkpoint	Final val mean@16	Final checkpoint
Tool-use / tooluse	RLSD_TR	Qwen3-4B	32	61.03%	60	59.38%	100

Training and validation scores

Validation Mean@16

step	val_mean16	percent
10	0.576286764706	57.63%
20	0.586397058824	58.64%
30	0.575367647059	57.54%
40	0.570772058824	57.08%
50	0.553308823529	55.33%
60	0.610294117647	61.03%
70	0.594669117647	59.47%
80	0.592830882353	59.28%
90	0.531250000000	53.12%
100	0.593750000000	59.38%

Detailed Training Hyperparameters

Section	Parameter	Value	Source
Run identity	`Base model`	`Qwen/Qwen3-4B`	queue/script override
Run identity	`Dataset`	`Tool-use / tooluse`	run_qwen3_generalization.sh
Run identity	`Method`	`RLSD_TR`	run_qwen3_generalization.sh
Run identity	`Config`	`rlsd`	run_qwen3_generalization.sh
Run identity	`Experiment`	`qwen3gen-tooluse-RLSD_TR-Qwen-Qwen3-4B-mbs8-decay0-tr0.1-train32-rollout8-lr1e-6-vllm0.8`	run_qwen3_generalization.sh
Run identity	`W&B run`	`run-20260702_154716-jrdzd11b`	wandb
Data	`Train file`	`datasets/tooluse/train.parquet`	script override
Data	`Validation file`	`datasets/tooluse/test.parquet`	script override
Data	`Train batch size`	`32`	queue/script override
Data	`Train max samples`	`3200`	queue/script override
Schedule	`Total training steps`	`100`	queue/script override
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
Rollout	`Train rollout n`	`8`	queue/script override
Rollout	`Validation rollout n`	`16`	queue/script override
Rollout	`vLLM GPU memory utilization`	`0.8`	queue/script override
Optimization	`Learning rate`	`1e-6`	RLSD_TR method override
Optimization	`Weight decay`	`0.01`	script override
PPO/GRPO	`PPO mini batch size`	`8`	queue/script override
PPO/GRPO	`Normalize GRPO advantages by std`	`False`	baseline_grpo.yaml / script override
Rollout correction	`Importance sampling mode`	`token`	script override
Rollout correction	`IS threshold`	`2.0`	script override
Checkpoint/Logging	`Checkpoint root`	`checkpoints/datasets/tooluse/qwen3gen-tooluse-RLSD_TR-Qwen-Qwen3-4B-mbs8-decay0-tr0.1-train32-rollout8-lr1e-6-vllm0.8`	script override
Checkpoint/Logging	`Latest checkpointed iteration`	`100`	latest_checkpointed_iteration.txt
Checkpoint/Logging	`External actor archive`	`checkpoints/datasets/tooluse/qwen3gen-tooluse-RLSD_TR-Qwen-Qwen3-4B-mbs8-decay0-tr0.1-train32-rollout8-lr1e-6-vllm0.8/_actor_archive`	preserve_actor_checkpoints.py
Checkpoint/Logging	`Logger`	`console, wandb`	ppo_trainer.yaml
RLSD_TR	`Policy loss mode`	`rlsd`	method override
RLSD_TR	`Teacher regularization`	`trust-region`	method override
RLSD_TR	`Trust-region mix / teacher update rate`	`0.1`	queue/script override
RLSD_TR	`Token reweight lambda`	`0.5`	queue/script override
RLSD_TR	`Token reweight eps_w`	`0.2`	queue/script override
RLSD_TR	`Token reweight decay steps`	`0`	queue/script override
RLSD_TR	`Fused kernels`	`False`	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

from transformers import AutoModelForCausalLM, AutoTokenizer

repo_id = "SeongryongJung/Qwen3-4B-Tooluse-RLSD-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/tooluse/qwen3gen-tooluse-RLSD_TR-Qwen-Qwen3-4B-mbs8-decay0-tr0.1-train32-rollout8-lr1e-6-vllm0.8
Root actor checkpoint: checkpoints/datasets/tooluse/qwen3gen-tooluse-RLSD_TR-Qwen-Qwen3-4B-mbs8-decay0-tr0.1-train32-rollout8-lr1e-6-vllm0.8/_actor_archive/global_step_60/actor
Last actor checkpoint: checkpoints/datasets/tooluse/qwen3gen-tooluse-RLSD_TR-Qwen-Qwen3-4B-mbs8-decay0-tr0.1-train32-rollout8-lr1e-6-vllm0.8/global_step_100/actor
W&B run: run-20260702_154716-jrdzd11b
Queue log: artifacts/queue.log

Task: Text Generation
Library: transformers
Type: qwen3
License: apache-2.0
Created: Jul 3, 2026
Updated: Jul 3, 2026