arXiv:2510.08558v3 Announce Type: replace-cross Abstract: A long-term goal of language agents is to learn and improve through their own experience, ultimately outperforming humans in complex, real-world tasks. However, training agents from experience data with reinforcement learning remains difficult in many environments, which either lack verifiable rewards (e.g., websites) or require inefficient long-horizon rollouts (e.g., multi-turn tool use). As a result, most current agents rely on supervised fine-tuning on expert data, which is challenging to scale and generalizes poorly. This limitatio
The paper addresses a critical limitation in AI agent development, focusing on learning from early experience right as the field moves towards more autonomous systems.
This work directly impacts the scalability and generalizability of AI agents, moving beyond reliance on expensive, expert-curated datasets.
The paradigm shifts from supervised fine-tuning on expert data to agents learning and improving through their own early experience.
- · AI agent developers
- · Companies deploying autonomous AI
- · AI research institutions
- · Expert data annotators
- · Companies reliant on large, static datasets
AI agents will exhibit improved adaptability and performance in complex, real-world tasks.
The cost and time required to develop and deploy highly capable AI agents will decrease significantly.
This could accelerate the widespread adoption of AI agents across various industries, replacing more white-collar tasks.
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Read at arXiv cs.CL