…We address these demands with a closed agent loop in which a VLM orchestrates heterogeneous robot capabilities as interruptible tools . Unlike in virtual AI agents, the timing of decisions, actions and tool…
…At the Inter-Reliability level, Stream-R1 rescales each rollout 's loss by an exponential of a pretrained video reward score , so that rollout s with reliable supervision dominate optimization. At the…
…Online Adaptation for Self-Improving Foundation Agents Published on May 11 Submitted by Seth Karten on May 13 Princeton University Authors: Seth Karten , , , , , , , Abstract A self-improving AI system for embodied agents…
…By integrating these two paradigms, we unlock a new level of multimodal AI capabilities. Get this paper in your agent: hf papers read 2606.03603 Don't have the latest CLI? curl…
…Yuxiang Ji , , , , , , , , Abstract Agentic reinforcement learning for large language models leverages action data from human interactions as reference guidance to improve exploration and reduce dependence on costly supervised fine-tuning. AI-generated…
…Learning Self-Evolving Skills for Coding Agents (2026) SkillRevise: Improving LLM-Authored Agent Skills via Trace-Conditioned Skill Revision (2026) Socratic-SWE: Self-Evolving Coding Agents via Trace-Derived Agent Skills (2026…
…Towards Training a Multimodal Agentic Search Model from Scratch (2026) E3-TIR: Enhanced Experience Exploitation for Tool-Integrated Reasoning (2026) PRAISE: Prefix-Based Rollout Reuse in Agentic Search Training (2026) $S^3…
…AI-generated summary Reinforcement Learning with Verifiable Rewards (RLVR) has achieved great success in developing Large Language Models ( LLMs ) with chain-of-thought rollouts for many tasks such as math and coding…
…To preserve gradient unbiasedness despite using trajectory-conditioned features , we introduce a cross-rollout construction that predicts each rollout's value from an independent rollout's internal states. Because POISE estimates prompt…
…naively injecting degraded views during rollout induces reward poisoning , where perceptual occlusions trigger hallucinated trajectories and destabilize optimization. We propose ROMA, an RL fine-tuning framework that modifies the optimization dynamics to…