This hands-on problem-solving tutorial provides both a rigorous algorithmic and practical introduction to multi-turn RL finetuning for LLMs, and covers state-of-the-art multi-turn RL finetuning algorithms, turn-level vs. trajectory-level reward design, and production grade monitoring for reward hacking detection.
Zhe Wang, Sapana Chaudhary, Jiayu Li et al.· Proceedings of the 32nd ACM...· 0 citations
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...
Zhe Wang, Sapana Chaudhary, Jiayu Li et al.· Proceedings of the 32nd ACM...· 0 citations
This work introduces AutoWorldModel-Bench, a closed-loop benchmark in which frontier coding agents autonomously improve a provided base world model under a fixed compute budget, and offers a setting in which frontier coding agents can be evaluated on open-ended research rather than engineering-to-spec problems.
Marjan Moodi, Xuan Zhu, F. Silva et al.· 0 citations
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