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update-paper-index

Add or review research paper entries in TRL’s paper index. Organize each paper’s contribution, its connection to the implementation, and a Python configuration example using training settings supported by the paper.

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Add or review a paper entry in TRL's paper index. Use when a PR implements a method, algorithm, or training approach from a research paper, or when reviewing such a PR.

集成工具Hugging Face
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适用场景

  • Documenting a newly implemented research method
  • Checking paper index updates during PR review
  • Recording training settings from a paper

包含的文件(1)

  • SKILL.md1.6 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Update the paper index

Any PR that implements a method, algorithm, or training approach from a research paper must add a corresponding subsection to docs/source/paper_index.md. When reviewing such a PR, check that the file was updated.

Entry format

The file is organized as one ## section per method family (usually one per trainer), each holding ### subsections, one per paper.

Each entry contains:

  1. The paper title as the ### heading.
  2. A paper link line, using the Hugging Face paper page (same ID as arXiv), never an arxiv.org link:
    **📜 Paper**: https://huggingface.co/papers/<id>
    
  3. A few sentences on what the paper introduces and how it maps to TRL.
  4. A Python snippet showing the TRL config that reproduces the paper's setting, with the paper's hyperparameters quoted in comments:
    from trl import GRPOConfig, GRPOTrainer
    
    training_args = GRPOConfig(
        beta=0.001,  # "the KL coefficient to 0.001"
        num_generations=16,  # "For each question, we sample 16 outputs..."
    )
    
    When the paper doesn't specify hyperparameters, say so in a comment rather than inventing values.

Placement

  • If the paper belongs to an existing method family, add it under that ## section.
  • If it introduces a new trainer or family, add a new ## section with a one-line pointer to the trainer, e.g. Papers relating to the [GRPOTrainer].

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同一仓库的技能

Write and debug code for further training large language models with TRL. Match training methods to dataset formats, configure supervised or preference-based training, and set up reward functions through Python or the command line.

中文简介正在准备中。当前显示英语简介。

  • Training a language model on example responses
  • Training with preferred and rejected response pairs
  • Debugging GRPO reward functions and batch settings
huggingface/trl2万2026年10月17日 更新

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