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Evaluating seven LLM agents on the HQ-50 subset under a standardized budget, it is found that closed-book rediscovery is rare, at most 7 of 40 resolved problems, whereas live-web success mostly reflects retrieval of published solutions.
The proposed **Lean4AgenticRL** is the first framework that grounds multi-level process rewards in the unified Lean4 formalization schema for open-ended agentic RL, and provides multi-level rewards to support more effective agentic RL with the workflow-stage-normalized GRPO algorithm.
Large language models remain vulnerable to jailbreaks, and automated red teaming is the standard way to find jailbreaks in large language models at scale. Current methods either draw more samples at test time through search, rewriting, and tree expansion, or train a stronger attacker offline with reinforcement learning...
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