In-the-Flow Agentic System Optimization for Effective Planning and Tool Use

Stanford Researchers Released AgentFlow: In-the-Flow Reinforcement Learning RL for Modular, Tool-Using AI Agents

Guiding Exploration in Reinforcement Learning Through LLM-Augmented Observations

Shorter but not Worse: Frugal Reasoning via Easy Samples as Length Regularizers in Math RLVR

Neues RL-Framework GIFT vereint GRPO, DPO und UNA für bessere LLM‑Ausrichtung

OPTAGENT: Optimizing Multi-Agent LLM Interactions Through Verbal Reinforcement Learning for Enhanced Reasoning

Rewarding the Journey, Not Just the Destination: A Composite Path and Answer Self-Scoring Reward Mechanism for Test-Time Reinforcement Learning