RISED: Agentic Multi-environment Selection and Self-DistillationのためのRubrics
新しい方法RISEDが導入され、多様なインタラクティブ環境におけるLLMエージェントのトレーニングに焦点を当てています。これはプロンプトグループの選択に加え、環境間での学習率の変化を処理することを目的としています。
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誰に影響するか
- 1RISED
- を使用 →事実
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根拠
- AarXiv cs.AI一次情報2026-10-02 12:00
An LLM judge tags each rollout using a predefined rubric vocabulary shared across environments.
- AarXiv cs.AI一次情報2026-10-02 12:00
Across model backbones, RISED achieves the highest mean pass rate across environments and ranks first or second in every individual environment.
- AarXiv cs.AI一次情報2026-10-02 12:00
Abstract: Training a single LLM agent jointly across diverse interactive environments has attracted increasing attention as a route to generalist agents. Existing curriculum and data-selection strategies often allocate training at the environment level or prioritize local reward-based signals, without explicitly considering relationships between current rollouts across environments for prompt-gro…