Reinforcement Learning via Value Gradient Flow

2026-04-15 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

Researchers introduce Value Gradient Flow (VGF), a new paradigm for behavior-regularized reinforcement learning (RL) designed to prevent value over-optimization. VGF re-frames behavior-regularized RL as an optimal transport problem, mapping a reference distribution (e.g., offline dataset or base LLM) to an optimal policy distribution guided by value gradients. This approach eliminates explicit policy parameterization, offering scalability for large generative models and adaptive test-time scaling by adjusting the transport budget. VGF implicitly regularizes by controlling this transport budget. Extensive experiments show VGF significantly outperforms prior methods, achieving state-of-the-art results on offline RL benchmarks like D4RL and OGBench, as well as on LLM RL tasks.

Key takeaway

For research scientists developing behavior-regularized RL systems, VGF offers a novel, scalable approach that avoids the limitations of reparameterized policy gradients and conservative reject sampling. You should consider integrating VGF into your LLM RL finetuning or offline RL projects to potentially achieve superior performance and more adaptive control over regularization without explicit policy parameterization.

Key insights

Value Gradient Flow (VGF) re-imagines behavior-regularized RL as an optimal transport problem, guided by value gradients.

Principles

• Implicit regularization via transport budget.
• Value gradients guide policy distribution.
• Eliminates explicit policy parameterization.

Method

VGF solves an optimal transport problem via discrete gradient flow, where value gradients guide particles initialized from a reference distribution to an optimal policy distribution.

In practice

• Apply VGF to large generative models.
• Use VGF for LLM RL finetuning.
• Adjust transport budget for adaptive scaling.

Topics

• Behavior-Regularized RL
• Value Gradient Flow
• Optimal Transport
• Discrete Gradient Flow
• Offline RL Benchmarks

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.