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[AIEWF Preview] Multi-Turn RL for Multi-Hour Agents — with Will Brown, Prime Intellect

Latent Space: The AI Engineer Podcast

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Navigating Token Budgets in Reinforcement Learning

This chapter explores the intricate balance of efficiency and complexity in multi-turn reinforcement learning models, addressing challenges like reward hacking and model reliability. It focuses on the implications of token usage and constraints in enhancing model performance while managing computational resources effectively.

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