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This paper proposes ProReviewer, an LLM-based scientific peer review agent formulated as a Markov Decision Process, which proactively investigates papers by maintaining a structured review log and outperforms existing methods across multiple quality dimensions.
This paper introduces the Markov decision contest, a new problem model for reinforcement learning with pairwise preferences. It proves optimality guarantees for stationary policies, exact solvability in P, and presents a learning-efficient approximate algorithm.
This paper formulates adaptive sampling for large language models as a Markov decision process and trains a lightweight RL controller to balance correctness, latency, and computational cost, achieving improved trade-offs.
This paper introduces a critique-and-routing controller for multi-agent LLM systems that formulates coordination as a sequential decision problem. It uses policy gradients to optimize the controller for iterative refinement, outperforming baselines while reducing reliance on top-tier models.
This article explains the fundamentals of Markov Decision Processes (MDPs), a core framework in deep reinforcement learning, using an educational example of a student's daily decisions.