LiteCoder/LiteCoder-rl

LiteCoder Terminal RL environments

Published 6/26/2026 by

harbor run -d LiteCoder/LiteCoder-rl

license: mit task_categories:

  • text-generation configs:
  • config_name: default data_files:
    • split: train path: data/train.parquet tags:
  • terminal
  • rl
  • code

LiteCoder-Terminal-RL-preview

Paper | Code | Blog Post

This dataset contains 602 standardized Harbor terminal environments and was released as part of the paper LiteCoder-Terminal: Scaling Long-Horizon Terminal Environments for Learning Language Agents.

Unlike static text-only instructions, these environments are fully executable and are designed to support the training of terminal-based agents.

Environment Generation Pipeline

The lack of high-quality, executable training environments is currently a major challenge for training terminal agents. Raw task descriptions lack the execution feedback required for techniques like rejection sampling and reinforcement learning, so an executable environment is essential. We implemented a five-stage synthesis pipeline to convert sampled tasks into the Harbor format using the Claude Agent SDK:

  1. Task refinement: rewrite raw task descriptions into clear and testable instructions
  2. Environment setup: prepare the execution environment and required resources
  3. Reference solution generation: produce a working solution for the task
  4. Verifier creation: construct test cases and evaluation logic
  5. Harbor packaging: assemble everything into the standard Harbor format

pipeline_flowchart.drawio-2

Environment Structure

Each environment represents a standardized terminal task instance organized in the Harbor format, which is as the following:

├── instruction.md
├── task.toml
├── environment
   ├── Dockerfile
   └── ...
├── solution
   ├── solve.sh
   └── ...
└── tests
    ├── test.sh
    └── ...

Citation

@article{peng2026litecoderterminal,
  title={LiteCoder-Terminal: Scaling Long-Horizon Terminal Environments for Learning Language Agents},
  author={Peng, Xiaoxuan and Zhang, Kaiqi and Lu, Xinyu and Cao, Boxi and Lu, Yaojie and Lin, Hongyu and Han, Xianpei and Sun, Le},
  journal={arXiv preprint arXiv:2605.29559},
  year={2026}
}
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