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 NeSyFS, a neuro-symbolic fast-slow thinking framework for LLM agents under partial observability, using a knowledge graph for belief state representation and a twisted sequential M…
Researchers introduce masked diffusion language models (MDLMs) as steerable text-based world models for reinforcement learning, achieving up to 47% absolute gains over baselines in zero-shot transfer …
A new framework called Experience Memory Graph (EMG) offers a promising solution to the persistent challenge of AI agents failing in complex, long-horizon tasks by treating error recovery as a graph m…
Researchers introduced TREK (Teacher-Routed Exploration via Forward KL), a new AI training method that enhances learning through unconventional exploration strategies. TREK significantly improved perf…
Researchers introduced TREK, a method that improves AI problem-solving by using verified output trajectories to extend model learning. TREK boosted Qwen3-8B's performance on AIME 2024 from 36.9 to 40.…
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…