LATIDIA · Investigación

Intervención en el tiempo de implementación: desbloquear el aprendizaje de refuerzo antigénico más allá de los límites de las políticas

arXiv: 2610.13438v1Tipo de anuncio: nuevo Resumen: El aprendizaje por refuerzo (RL) se ha convertido en un paradigma clave para mejorar los agentes del modelo de lenguaje grande (LLM), lo que permite un razonamiento, planificación y uso de herramientas más sólidos en la interacción

La noticia

arXiv:2610.13438v1 Announce Type: new Abstract: Reinforcement learning (RL) has emerged as a key paradigm for improving large language model (LLM) agents, enabling stronger reasoning, planning, and tool-use in interactive environments. However, existing agent RL methods remain fundamentally constrained by an on-policy reasoning bottleneck: when tasks exceed the model's current capability boundary, effective trajectories become extremely sparse and training often collapses. We introduce Rollout-Time Intervention (RTI), a technique designed to elicit successful trajectories beyond the policy's intrinsic exploration capacity. RTI operates by leveraging reward-side evidence (i.e., evaluation rubrics) during rollout to convert erroneous trajectories into successful ones, providing effective learning signals for tasks that the policy rarely solves on its own.