Policy Iteration with Human Feedback: Bringing Post-Training RL to In-context Learning

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning

SEED (SElf-Evolving On-Policy Distillation), a self-evolving framework that converts completed on-policy trajectories into training-time hindsight skills and distills their behavioral effect back into the policy model, is proposed.

Cross-Benchmark Generalization in Long-Horizon Agents

Desc descriptive evidence is provided that long-horizon multi-tool post-training can change ways of working that transfer beyond its training domain, and both software-engineering benchmarks improve despite the training collection containing no software-engineering tasks.

Self-Review Reinforcement Learning (SRRL) with Cross-Episode Memory and Policy Distillation

Self-Review Reinforcement Learning consistently outperforms the RLVR in final reward performance and achieves greater learning efficiency by successfully transforming feedback into behavioral improvement.

Knowing When Not to Reuse: Conditional Experience Transfer in Autonomous LLM Post-Training

Conditional experience transfer is formulated as conditional experience transfer and Boundary-Calibrated Intervention Transfer is introduced, a method that authorizes experience reuse before weight-changing training and attains higher equal-budget final-model quality than the evaluated alternatives.

OLIVIA: Online Learning via Inference-time Action Adaptation

Multi-Turn Reinforcement Learning for Large Language Models: From Theory to Practice with Amazon SageMaker AI

Production large language model (LLM) based systems such as coding agents, web navigators, and tool-calling assistants operate over multiple turns of interaction with users, tools, and environments. Pretrained LLMs, depending on their size, can either underperform in these settings due to misalignment with the system's interaction mechanics, or, when capable, incur prohibitive latency. Fine-tuning right-sized models addresses both accuracy and latency, but training such multi-turn agents requires Reinforcement Learning (RL), where the model acts as a policy optimizing long-horizon outcomes across sequential interactions. This poses challenges absent from single-turn settings: credit assignment over long trajectories, reward design for sparse and delayed feedback, state and context management as observation histories grow, environment scaling for parallel rollout collection, and training stability under prompt/environment distribution shift. This hands-on problem-solving tutorial provides both a rigorous algorithmic and practical introduction to multi-turn RL finetuning for LLMs. Using Amazon SageMaker AI, participants progress through four labs: (1) environment and reward function design, (2) multi-turn trajectory collection and Group Relative Policy Optimization (GRPO)-based training, (3) reward densification and credit assignment strategies, and (4) evaluation, failure diagnosis and deployment. We cover state-of-the-art multi-turn RL finetuning algorithms, turn-level vs. trajectory-level reward design, and production grade monitoring for reward hacking detection. The tutorial targets machine learning (ML) engineers, data scientists, and researchers who build agentic LLM systems. No prior RL experience is required. All materials will be publicly available on GitHub.