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New framework allows language agents to learn from experience

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new framework called In-context Training (ICT) to enable language agents to learn and improve from past experiences across different tasks. This approach trains a "reflector" model to generate system prompts that enhance an "actor" model's performance on future, unseen tasks. Experiments in ALFWorld and MiniHack demonstrated that agents trained with this method showed improved performance on various task families, with some even generalizing to entirely new environments. AI

IMPACT Introduces a method for agents to generalize learning across tasks, potentially improving adaptability and efficiency in complex AI systems.

RANK_REASON The cluster contains an academic paper detailing a new framework and methodology for training language agents.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New framework allows language agents to learn from experience

COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Yuval Shalev, Zifeng Ding, Mateja Jamnik ·

    Training Language Agents to Learn from Experience

    arXiv:2605.20477v1 Announce Type: cross Abstract: Language agents can adapt from experience in interactive environments, but current reflection-based methods can only self-correct within a single task instance. Whether such experience can be distilled into reusable lessons that i…

  2. arXiv cs.CL TIER_1 English(EN) · Mateja Jamnik ·

    Training Language Agents to Learn from Experience

    Language agents can adapt from experience in interactive environments, but current reflection-based methods can only self-correct within a single task instance. Whether such experience can be distilled into reusable lessons that improve performance on future unseen tasks remains …

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