Hongyao Tang

Judgment models can solve complex problems when the answer is directly evaluable from the input, but fail catastrophically when simulation is required, even if they possess the underlying knowledge.

This paper proposes Generalized Agent Iteration (GAI), a formal framework that describes iterative policy improvement and RSI as two cases of a single learning paradigm that rests on the classical account, makes existing systems comparable, and provides a principled basis for analyzing and designing new ones.

WarpSAC redefines off-policy RL by tailoring stabilizers to data availability, achieving up to 96.4% success rates in challenging environments.

Memory systems can now be quantitatively assessed through a utility-capacity frontier, revealing how to optimize agent memory for better performance.

Training updates that improve performance in LLMs can actually degrade inference quality—unless you use the new Monotonic Inference Policy Update framework.

Plasticity loss in RL isn't just about forgetting; it's about vanishing gradients, and a simple sample re-weighting can bring back the learning.