---
license: apache-2.0
language:
- en
tags:
- long-context
- reinforcement-learning
- reasoning
- rubric-reward
- qwen3
base_model:
- Qwen/Qwen3-4B
---

# LongTraceRL-4B

[![Paper](https://img.shields.io/badge/Paper-arXiv:2605.31584-red)](https://arxiv.org/abs/2605.31584)
[![Code](https://img.shields.io/badge/Code-GitHub-blue)](https://github.com/THU-KEG/LongTraceRL)

## Model Description

**LongTraceRL-4B** is a 4-billion parameter reasoning model trained with reinforcement learning on long-context multi-hop QA tasks using trajectory-based tiered distractors and entity-level rubric rewards.

## Model Details

- **Base Model**: Qwen3-4B-Thinking-2507
- **Parameters**: 4B
- **Architecture**: Qwen3 (36 layers, hidden size 2560, GQA with 8 KV groups)
- **Training Method**: GRPO with entity-level rubric reward
- **Context Length**: 128K prompt + 32K response
- **Language**: English

## Training Details

- **Training Data**: 2,815 long-context multi-hop QA samples ([LongTraceRL Dataset](https://huggingface.co/datasets/THU-KEG/LongTraceRL))
- **Training Steps**: 200
- **Learning Rate**: 2e-6 (constant)
- **Global Batch Size**: 128
- **GRPO Group Size**: 8
- **Rubric Reward Weight (η)**: 0.3
- **Framework**: [Slime](https://github.com/THUDM/slime) (Megatron-LM + SGLang)

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("THU-KEG/LongTraceRL-4B")
tokenizer = AutoTokenizer.from_pretrained("THU-KEG/LongTraceRL-4B")
```

## Citation

```bibtex
@misc{lin2026longtracerllearninglongcontextreasoning,
      title={LongTraceRL: Learning Long-Context Reasoning from Search Agent Trajectories with Rubric Rewards}, 
      author={Nianyi Lin and Jiajie Zhang and Lei Hou and Juanzi Li},
      year={2026},
      eprint={2605.31584},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2605.31584}, 
}
```
