arXiv:2605.20477v1 Announce Type: new 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 improve performance on future unseen tasks remains unclear. We address this problem by introducing the In-context Training (ICT) task, a framework for evaluating cross-task self-improvement in language agents. In ICT, a reflector model observes trajectories collected by an actor model and generates system prompts intended
The proliferation of language models and growing interest in autonomous agents highlight the limitations of current reflection-based learning, driving the need for more sophisticated, cross-task self-improvement mechanisms.
This work introduces a framework for evaluating and developing language agents that can distill experience into reusable lessons, potentially accelerating the development of truly autonomous and general-purpose AI.
The focus shifts from single-task self-correction to cross-task self-improvement, allowing agents to leverage past experiences for better performance on unseen future tasks.
- · AI research labs
- · Developers of AI agents
- · Industries adopting autonomous systems
- · Companies relying on static AI models
- · Inefficient workflow automation tools
More robust and adaptable AI agents capable of continuous learning across diverse environments will emerge.
The ability of AI to autonomously improve will collapse more white-collar workflows, leading to significant productivity gains but also job displacement.
The acceleration of AI capabilities through experiential learning could lead to earlier achievement of artificial general intelligence or superintelligence, with profound societal implications.
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Read at arXiv cs.LG