New Methods for LLM Agent Training and Decision-Making
wzenus · x · 2026-07-10
CMU researchers presented two related directions in LLM-agent training. First, they noted that scalar rewards can lead to failure modes, proposing an improvement using richer text feedback and self-distillation. This method, named MaxRL, stabilizes training by normalizing updates based on task difficulty.
The session also introduced Calibrate-Then-Act, a framework enabling language model agents to make better decisions under uncertainty and cost constraints, such as knowing when to retrieve, ask for clarification, experiment, or stop.
Related event: ICML 2026 FAGEN Workshop Spotlights AI Agent Failure Modes(11 posts)→
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