---
language:
- en
pretty_name: HERO Evaluation Trajectories
size_categories:
- 1K<n<10K
tags:
- agent-trajectories
- coding-agents
- swe-bench
- evaluation
---

<p align="center">
  <img src="assets/hero-logo.png" alt="HERO logo" width="760">
</p>

<h1 align="center">HERO Evaluation Trajectories</h1>

<p align="center">
  <strong>Doing More with Less Tokens: Hierarchical Reinforcement Learning for Efficient Coding Agents</strong>
</p>

<p align="center">
  <a href="https://arxiv.org/abs/2609.38885"><img src="https://img.shields.io/badge/arXiv-2609.38885-b31b1b.svg" alt="Paper"></a>
  <a href="https://github.com/XLearning-SCU/HERO"><img src="https://img.shields.io/badge/GitHub-Code-181717.svg" alt="Code"></a>
  <a href="https://huggingface.co/collections/XLearning-SCU/hero"><img src="https://img.shields.io/badge/%F0%9F%A4%97-Models-f4c430.svg" alt="Hugging Face Models"></a>
  <a href="https://huggingface.co/datasets/XLearning-SCU/HERO-Trajectories"><img src="https://img.shields.io/badge/%F0%9F%A4%97-Trajectories-f4c430.svg" alt="Hugging Face Trajectories"></a>
</p>

## 🔍 Overview

HERO is a hierarchical reinforcement learning method for training token-efficient coding agents while prioritizing task resolution. This release contains 2,400 evaluation trajectories from HERO-4B, HERO-9B, and HERO-35B-A3B on SWE-bench Verified and SWE-bench Multilingual, using Claude Code as the agent scaffold.

## Results

Each model includes 500 Verified tasks and 300 Multilingual tasks. These are individual recorded runs; the paper reports averages over three random seeds.

| Benchmark | Model | Tasks | Resolved Rate | Token Usage (M) |
| --- | --- | ---: | ---: | ---: |
| Verified | [HERO-4B](verified/HERO-4B/) | 500 | 43.2% | 3.5 |
| Verified | [HERO-9B](verified/HERO-9B/) | 500 | 50.6% | 2.8 |
| Verified | [HERO-35B-A3B](verified/HERO-35B-A3B/) | 500 | 61.4% | 2.4 |
| Multilingual | [HERO-4B](multilingual/HERO-4B/) | 300 | 29.3% | 4.2 |
| Multilingual | [HERO-9B](multilingual/HERO-9B/) | 300 | 40.0% | 3.6 |
| Multilingual | [HERO-35B-A3B](multilingual/HERO-35B-A3B/) | 300 | 46.0% | 2.7 |

Tokens are averages per task in millions (M), summed over agent calls, including repeated input context. Records with missing or zero usage are excluded from token averages; resolve rates include all tasks. Detailed usage and processing notes are in each `summary.json`.

## Files

Each `<benchmark>/<model>/` directory contains:

| File | Contents |
| --- | --- |
| `results.jsonl` | Task outcomes, grading reports, token usage, and trajectory paths |
| `predictions.jsonl` | Predicted patches |
| `summary.json` | Aggregate results, evaluation settings, and processing notes |
| `trajectories.tar.gz` | Per-task JSON trajectories in `hero-trace-v1` format |

## Loading the Traces

Extract an archive from the repository root:

```bash
tar -xzf verified/HERO-4B/trajectories.tar.gz -C verified/HERO-4B
```

Load an extracted trajectory:

```python
import json
from pathlib import Path

path = next(Path("verified/HERO-4B/trajectories").glob("*.json"))
with path.open() as f:
    trace = json.load(f)
calls = trace["trajectory"]
```

The `trajectory_file` paths in `results.jsonl` are relative to the model directory.

## Run Notes

The existing 35B run summaries report different checkpoints for Verified and Multilingual; checkpoint correspondence with the released weights remains unconfirmed. Some prediction patches were sanitized without re-evaluation, so resolution labels describe the original runs. The 35B Multilingual token statistics exclude auxiliary token-counting calls, as documented in its `summary.json`.

## Source Licenses

[SWE-bench Verified](https://huggingface.co/datasets/princeton-nlp/SWE-bench_Verified), [SWE-bench Multilingual](https://huggingface.co/datasets/SWE-bench/SWE-bench_Multilingual), and their source repositories retain their respective licenses. Please also cite the corresponding benchmarks when using these traces.

## 📖 Citation

If you find HERO useful, please cite our [paper](https://arxiv.org/abs/2609.38885):

```bibtex
@misc{li2026hero,
  title         = {Doing More with Less Tokens: Hierarchical Reinforcement Learning for Efficient Coding Agents},
  author        = {Haobin Li and Liang Jiang and Zhenyu Huang and Mouxing Yang and Xi Peng},
  year          = {2026},
  eprint        = {2609.38885},
  archivePrefix = {arXiv},
  primaryClass  = {cs.SE},
  url           = {https://arxiv.org/abs/2609.38885}
}
```