…Diverse Parallel Exploration Policy Optimization for LLM-based Agents (2026) COMAP: Co-Evolving World Models and Agent Policies for LLM Agents (2026) Enhancing LLM-based Search Agents via Contribution Weighted Group Relative…
…it only needs to model a target-frame transformation, focuses on action-relevant current-to-target visual differences, and grounds task instructions to localized visual changes through edit pretraining. In practice, ImageWAM…
…Co-Evolving World Models and Agent Policies for LLM Agents (2026) Beyond State Consistency: Behavior Consistency in Text-Based World Models (2026) GAPD: Gold-Action Policy Distillation for Agentic Reinforcement Learning in…
…View arXiv page View PDF Add to collection Community Can a single denoising step determine the fate of an entire robot policy? In this work, we show that perturbing only the earliest…
…positive for a useful call, negative for a misleading one, and zero for one that changes nothing. This reuses the existing answer checker with no auxiliary judge, and, being a difference rather…
…Frontier models are closed, open-weight alternatives are tied to expensive hardware, reasoning-augmented policies pay prohibitive latency for their grounding, and fine-tuned success rates remain below the threshold for dependable…
…Latent World Action Models for Efficient Dynamics-Aware Robot Policies (2026) SG-WAM: Self-Guided World Modeling in Geometry-Aware Policy Space (2026) LiLa-WAM: Lightweight Latent Reasoning World-Action Model for…
…high-quality demonstrations are expensive to collect, and static datasets offer limited coverage of the diverse, ever-changing open web. Although online RL has shown promise for text-based agents, its potential…
…Jungsuk Oh , , , , Abstract SPEED is a phase-asymmetric KV-visibility policy that reduces long-context inference costs in decoder-only language models by processing prompt tokens in lower layers during prefill while…
…As a proof of concept, we present a new Generalized Distillation process for { Knowledge Seeding } (i.e., the combination of on-policy distillation with Reinforcement Learning (RL)-based imitation learning); (2) Dreaming…