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cs.LG2026
Learning to Solve Hard Problems in RL for LLMs by Never Giving Up
Michael Noukhovitch, Hamish Ivison, Nathan Lambert +1
We demonstrate that training LLMs with RL does not improve performance equally across a dataset. RL shows large improvements on easy problems that an LLM is already good at solving…
cs.LG2026
Meta-Reinforcement Learning with Self-Reflection for Agentic Search
Teng Xiao, Yige Yuan, Hamish Ivison +6
This paper introduces MR-Search, an in-context meta reinforcement learning (RL) formulation for agentic search with self-reflection. Instead of optimizing a policy within a single…