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

2026-05-28 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

RL2ML is a newly developed family of finite-rollout surrogate objectives designed for training language models using correctness-based Reinforcement Learning with Verifiable Rewards (RLVR). This framework provides a closed-form, exactly unbiased gradient estimator and continuously connects standard reinforcement learning with maximum-likelihood-like and beyond-maximum-likelihood training objectives, maintaining estimator-objective alignment under fixed rollout budgets. The research introduces the concept of a group-level update scale, which characterizes how a rollout group is reweighted based on its empirical success count, revealing a previously hidden subcritical-supercritical update-scale transition. Crucially, calibrated metric-gain analysis and exact variance decomposition demonstrate that the optimal surrogate objective choice is determined by the evaluation metric, local sensitivity, and estimator variance, rather than solely by its proximity to maximum likelihood or population-level weight. This allows the remaining degree of freedom in the objective family to be formulated as a one-dimensional optimization problem.

Key takeaway

For Machine Learning Engineers developing language models with correctness-based RLVR, you should critically evaluate your surrogate objective choice beyond simple maximum likelihood proximity. Utilize RL2ML's framework to formulate the remaining objective freedom as a one-dimensional optimization problem, ensuring your objective aligns with specific evaluation metrics, local sensitivity, and estimator variance. This approach will lead to more robust and efficient training outcomes, moving beyond conflated objective expectations.

Key insights

RL2ML offers unbiased, finite-rollout surrogate objectives connecting RL and ML, with optimal choice depending on metrics and variance.

Principles

• Objective choice depends on metric, sensitivity, variance.
• Group-level update scale reveals hidden transitions.
• Unbiased gradient estimators are achievable.

Method

RL2ML develops a family of finite-rollout surrogate objectives with a closed-form, exactly unbiased gradient estimator, allowing the remaining degree of freedom to be optimized.

In practice

• Optimize surrogate objective as 1D problem.
• Calibrate objective choice to evaluation metrics.
• Consider local sensitivity and estimator variance.

Topics

• RL2ML
• Reinforcement Learning
• Maximum Likelihood Training
• Surrogate Objectives
• Gradient Estimators
• Language Model Training

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.