Researchers propose TurnOPD, a turn-level budgeting strategy for efficient on-policy distillation of long-horizon language agents. The method addresses inefficiencies in vanilla agent OPD by using ada…
Microsoft researchers developed SkillOpt, a method that treats AI agent skill files as trainable parameters outside frozen target models, enabling controlled optimization through bounded text edits an…
Memory-augmented AI agents using TraceRetain show improved performance in clean environments, but retention strategies yield smaller differences than expected. In noisy settings, TraceRetain-CEM maint…
Researchers introduced RSEA, a Recursive Self-Evolving Agent that improves LLM agents by rewriting natural-language artifacts without weight updates, using a held-out selection gate to prevent regress…
Researchers propose ATOD, a hybrid online distillation algorithm that combines on-policy distillation and reinforcement learning to train small language-model agents for multi-turn tasks. ATOD uses an…
Researchers introduced a prompt-based uncertainty decomposition method that separates action confidence from request uncertainty, enabling LLM agents to proactively seek clarification when task specif…
Researchers have developed CVT-RL, a reinforcement learning algorithm that uses policy-conditioned counterfactual credit assignment to reduce unsupported evidence chains and shortcut actions in long-h…
Microsoft Research has released SkillLens, an open-source framework designed as a "microscope" for analyzing how AI agents absorb and utilize skills. The framework provides a complete pipeline for ext…
Researchers have developed S3MEM, a structured memory framework that improves long-horizon interactive question answering by converting agent trajectories into query-aligned evidence. In tests across …