6 papers
MaxProof: Scaling Mathematical Proof with Generative-Verifier RL and Population-Level Test-Time Scaling
Jiacheng Chen, Xinyu Zhang, Shunkai Zhang +20
We present MaxProof, a population-level test-time scaling framework for competition-level mathematical proof in the MiniMax-M3 series. M3 first trains three proof-oriented capabili…
Interactive Critique-Revision Training for Reliable Structured LLM Generation
Fei Xu Yu, Zuyuan Zhang, Mahdi Imani +2
In structured decision-making workflows such as form filling, compliance checking, and maintenance reporting, LLM outputs must be locally correct, globally consistent, and auditabl…
Operator-Guided Invariance Learning for Continuous Reinforcement Learning
Zuyuan Zhang, Fei Xu Yu, Tian Lan
Reinforcement learning (RL) with continuous time and state/action spaces is often data-intensive and brittle under nuisance variability and shift, motivating methods that exploit v…
ACDZero: MCTS Agent for Mastering Automated Cyber Defense
Yu Li, Sizhe Tang, Rongqian Chen +5
Automated cyber defense (ACD) seeks to protect computer networks with minimal or no human intervention, reacting to intrusions by taking corrective actions such as isolating hosts,…
Optimizing Prompt Sequences using Monte Carlo Tree Search for LLM-Based Optimization
Fei Xu Yu, Gina Adam, Nathaniel D. Bastian +1
Mathematical optimization is a core tool in engineering, operations research, and data science, but modeling real problems remains difficult and time-consuming. In practice, an exp…
Advantage Actor-Critic with Reasoner: Explaining the Agent's Behavior from an Exploratory Perspective
Muzhe Guo, Feixu Yu, Tian Lan +1
Reinforcement learning (RL) is a powerful tool for solving complex decision-making problems, but its lack of transparency and interpretability has been a major challenge in domains…