| --- |
| 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} |
| } |
| ``` |
|
|
