arXiv AI
CoFlow: Coordinated Few-Step Flow for Offline Multi-Agent Decision Making
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arXiv AI
Aug 3
RAPiD: Reward-Guided Consistency Distillation of Diffusion Planners for Real-Time Autonomous Driving
arXiv:2602. 07339v2 Announce Type: replace Abstract: Diffusion-based trajectory planners can model multi-modal driving behavior, but their iterative denoising process introduces a latency bottleneck for real-time closed-loop deployment.
arXiv AI
Jul 23
In-the-Flow Agentic System Optimization for Effective Planning and Tool Use
arXiv:2510. 05592v2 Announce Type: replace Abstract: Outcome-driven reinforcement learning has advanced reasoning in large language models (LLMs), but prevailing tool-augmented approaches train a single, monolithic policy that interleaves thoughts and tool calls under full context; this scales poorly with long horizons and diverse tools and generalizes weakly to new scenarios.
arXiv AI
Jun 2
MAVEN-T: Reinforced Heterogeneous Distillation for Real-Time Multi-Agent Trajectory Prediction
arXiv:2604. 10169v2 Announce Type: replace Abstract: Trajectory prediction is a key component of autonomous driving systems because future motions directly affect collision checking, behavior planning, and control.
arXiv AI
Sep 10
AgentBrew: Offline Tool-Use Agent Learning from Raw Real-World Trajectories
arXiv:2609.05837v1 Announce Type: new Abstract: LLM-based agents are increasingly deployed in real-world applications through tool-use APIs, yet training them for specific environments remains fundam...
arXiv AI
Sep 7
CoSkill: Joint Reinforcement Learning of Reasoning and Meta-Skill Agents for Hierarchical Skill Evolution
CoSkill introduces a unified multi‑agent reinforcement learning framework that jointly trains a Reasoning Agent and a learnable Meta‑Skill Agent over a hierarchical skill library. By treating the meta‑skill workflow as a trainable agent and sharing a single backbone, CoSkill enables end‑to‑end co‑adaptation, allowing the Reasoning Agent to condition actions on retrieved task and step skills while the Meta‑Skill Agent refines those skills based on task performance. Experiments on ALFWorld and WebShop demonstrate that CoSkill outperforms prior skill‑based and RL baselines, achieving higher success rates and improved sample, asymptotic, and wall‑clock efficiency.
arXiv Machine Learning
Aug 4
Cooperative Coevolution for Resource-Constrained Agentic LLM Post-Training
arXiv:2608. 02391v1 Announce Type: cross Abstract: Tool-using large language model (LLM) agents produce long, multi-turn trajectories, making gradient-based post-training memory-intensive.