LLM Digest

Story

arxiv_agent_systems_research · Nov 12, 2026 · paper

Source brief

StateAlign: Internalizing State-Aware Decision-Making for Reliable Multi-Turn Tool Agents via Reinforcement Learning

arxiv.orgNov 12, 2026
original source linked

In brief

Reinforcement learning has improved the tool-use capabilities of large language models. However, better task performance can coincide with increased tool hallucination. Reliable tool use requires decisions to follow the current task state: available information, tool capabilities

Read the original at arxiv.org →Open in live feed Read that day’s brief

StateAlign: Internalizing State-Aware Decision-Making for Reliable Multi-Turn Tool Agents via Reinforcement Learning

Earlier in this thread 4 items

What Does Multi-Turn Reinforcement Learning Really Improve in LLM Agents?

Multi-Turn Reinforcement Learning on Amazon SageMaker AI: A hands-on guide

Best practices for multi-turn reinforcement learning in Amazon SageMaker AI

ReShap: Interpretable Reinforcement Learning for High-Dimensional Multi-Action Settings