Papers2

#KL-divergence

Agent-Omit: Training Efficient LLM Agents for Adaptive Thought and Observation Omission via Agentic Reinforcement Learning

Agent-Omit teaches AI agents to skip unneeded thinking and old observations, cutting tokens while keeping accuracy high.

#LLM agents#reinforcement learning#agentic RL

Not triaged yet

PRL: Process Reward Learning Improves LLMs' Reasoning Ability and Broadens the Reasoning Boundary

Intermediate

Jiarui Yao, Ruida Wang et al.Jan 15arXiv

Large language models usually get only a final thumbs-up or thumbs-down at the end of their answer, which is too late to fix mistakes made in the middle.

#Process Reward Learning#PRL#Reasoning LLMs

Not triaged yet