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RL2ML: Finite-Rollout Surrogate Objectives from Reinforcement Learning to Maximum Likelihood

arXiv — cs.LG•Friday, May 29, 2026 at 4:00:00 AM
NeutralArtificial Intelligence
  • What Happened

    The paper titled 'RL2ML: Finite-Rollout Surrogate Objectives from Reinforcement Learning to Maximum Likelihood' introduces a new family of finite-rollout surrogate objectives that provide a closed-form, unbiased gradient estimator for training language models using Reinforcement Learning with Verifiable Rewards (RLVR). This development addresses the conflation of expected objectives and stochastic update geometries in existing methods.

  • Why It Matters

    The introduction of RL2ML is significant as it enhances the training efficiency of language models by ensuring estimator-objective alignment within a fixed rollout budget. This advancement could lead to improved performance in various applications of language models, particularly in environments where binary feedback is available.

  • The Bigger Picture

    This research contributes to ongoing discussions in the field of reinforcement learning, particularly regarding the balance between maximum likelihood training and reinforcement learning objectives. It aligns with recent studies exploring the functional welfare axis in language models and the challenges of catastrophic forgetting, highlighting the evolving landscape of AI training methodologies.

— via World Pulse Now AI Editorial System
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