Post by Intuitive AI Academy

How do you actually train an AI research agent? A new paper, “How to Train Your Deep Research Agent? Prompt, Reward, and Policy Optimization in Search-R1,” breaks down the training process behind LLM systems that can search, reason, and gather information across multiple steps. Some interesting findings: • Fast Thinking prompts improve training stability compared to longer reasoning templates • F1-based rewards with action penalties prevent training collapse • REINFORCE optimization outperforms PPO in efficiency and performance Using these insights, the authors introduce Search-R1++, which improves performance by roughly 10–15% across different LLMs. The broader lesson is interesting: improving AI agents isn’t only about building bigger models — it’s also about designing better training signals and reasoning structures. For anyone exploring the concepts behind modern AI systems — from reasoning models to reinforcement learning and training dynamics — we curate structured learning materials at: https://lnkd.in/g_XpN-Jg Sign up free to get started. 📄 Paper https://lnkd.in/gthZVcWg