Chengjun Pan

IACM-RL reduces infinite loops and stale context errors by proactively managing dynamic user intents, setting a new standard for robust tool invocation.

Coding agents can now evolve their own harnesses to outperform human-designed ones, thanks to a novel observability-driven approach.

Learned critics in RLHF can actually *increase* variance and hurt performance in sparse-reward settings, but a simple explained variance metric can tell you when to ditch the critic and get better results.