Researchers propose Stateful Knowledge Learning (SKL), a method that trains large language model agents to maintain explicit, state-anchored predictive knowledge instead of relying on trajectory-level…
Researchers propose CAST (Credit Assignment from Solver Teachers), a method that converts game solver state-value changes into turn-level signals to improve reinforcement learning for LLM agents in lo…
Researchers propose Progress-conditioned Group Policy Optimization (ProGPO), a method that uses first-visit observation coverage to assign higher advantages to trajectories visiting new states when al…
Researchers introduce Branching Policy Optimization (BPO), a reinforcement learning algorithm for LLM agents that exploits deterministic, snapshottable sandboxes to reduce variance and improve trainin…
Researchers have introduced normalized entropy and the STAPO framework to address trajectory neglect in reinforcement learning, where AI models lose track of goals during complex tasks. Tests on ALFWo…
Researchers introduced TRIAGE, a novel framework that refines credit assignment in agentic reinforcement learning by classifying actions based on their role, improving success rates and reducing ineff…
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…
Researchers introduced Agentic Abstention, a framework for LLM agents to decide when to stop acting under uncertainty, and found that many agents fail to abstain or do so too late. Testing 13 LLM agen…
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…