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Upload HERO-Trajectories release

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.gitattributes CHANGED
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+ verified/HERO-35B-A3B/results.jsonl filter=lfs diff=lfs merge=lfs -text
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+ verified/HERO-4B/results.jsonl filter=lfs diff=lfs merge=lfs -text
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+ verified/HERO-9B/results.jsonl filter=lfs diff=lfs merge=lfs -text
README.md CHANGED
@@ -1,3 +1,105 @@
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  ---
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- license: apache-2.0
 
 
 
 
 
 
 
 
 
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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+ language:
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+ - en
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+ pretty_name: HERO Evaluation Trajectories
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+ size_categories:
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+ - 1K<n<10K
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+ tags:
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+ - agent-trajectories
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+ - coding-agents
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+ - swe-bench
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+ - evaluation
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  ---
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+
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+ <p align="center">
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+ <img src="assets/hero-logo.png" alt="HERO logo" width="760">
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+ </p>
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+
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+ <h1 align="center">HERO Evaluation Trajectories</h1>
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+
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+ <p align="center">
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+ <strong>Doing More with Less Tokens: Hierarchical Reinforcement Learning for Efficient Coding Agents</strong>
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+ </p>
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+
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+ <p align="center">
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+ <a href="https://arxiv.org/abs/2609.38885"><img src="https://img.shields.io/badge/arXiv-2609.38885-b31b1b.svg" alt="Paper"></a>
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+ <a href="https://github.com/XLearning-SCU/HERO"><img src="https://img.shields.io/badge/GitHub-Code-181717.svg" alt="Code"></a>
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+ <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>
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+ <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>
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+ </p>
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+
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+ ## 🔍 Overview
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+
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+ 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.
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+
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+ ## Results
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+
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+ Each model includes 500 Verified tasks and 300 Multilingual tasks. These are individual recorded runs; the paper reports averages over three random seeds.
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+
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+ | Benchmark | Model | Tasks | Resolved Rate | Token Usage (M) |
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+ | --- | --- | ---: | ---: | ---: |
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+ | Verified | [HERO-4B](verified/HERO-4B/) | 500 | 43.2% | 3.5 |
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+ | Verified | [HERO-9B](verified/HERO-9B/) | 500 | 50.6% | 2.8 |
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+ | Verified | [HERO-35B-A3B](verified/HERO-35B-A3B/) | 500 | 61.4% | 2.4 |
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+ | Multilingual | [HERO-4B](multilingual/HERO-4B/) | 300 | 29.3% | 4.2 |
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+ | Multilingual | [HERO-9B](multilingual/HERO-9B/) | 300 | 40.0% | 3.6 |
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+ | Multilingual | [HERO-35B-A3B](multilingual/HERO-35B-A3B/) | 300 | 46.0% | 2.7 |
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+
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+ 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`.
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+
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+ ## Files
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+
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+ Each `<benchmark>/<model>/` directory contains:
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+
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+ | File | Contents |
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+ | --- | --- |
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+ | `results.jsonl` | Task outcomes, grading reports, token usage, and trajectory paths |
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+ | `predictions.jsonl` | Predicted patches |
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+ | `summary.json` | Aggregate results, evaluation settings, and processing notes |
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+ | `trajectories.tar.gz` | Per-task JSON trajectories in `hero-trace-v1` format |
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+
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+ ## Loading the Traces
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+
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+ Extract an archive from the repository root:
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+
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+ ```bash
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+ tar -xzf verified/HERO-4B/trajectories.tar.gz -C verified/HERO-4B
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+ ```
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+
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+ Load an extracted trajectory:
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+
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+ ```python
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+ import json
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+ from pathlib import Path
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+
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+ path = next(Path("verified/HERO-4B/trajectories").glob("*.json"))
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+ with path.open() as f:
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+ trace = json.load(f)
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+ calls = trace["trajectory"]
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+ ```
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+
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+ The `trajectory_file` paths in `results.jsonl` are relative to the model directory.
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+
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+ ## Run Notes
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+
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+ 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`.
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+
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+ ## Source Licenses
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+
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+ [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.
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+
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+ ## 📖 Citation
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+
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+ If you find HERO useful, please cite our [paper](https://arxiv.org/abs/2609.38885):
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+
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+ ```bibtex
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+ @misc{li2026hero,
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+ title = {Doing More with Less Tokens: Hierarchical Reinforcement Learning for Efficient Coding Agents},
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+ author = {Haobin Li and Liang Jiang and Zhenyu Huang and Mouxing Yang and Xi Peng},
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+ year = {2026},
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+ eprint = {2609.38885},
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+ archivePrefix = {arXiv},
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+ primaryClass = {cs.SE},
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+ url = {https://arxiv.org/abs/2609.38885}
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+ }
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+ ```
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