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offline-reinforcement-learning

topic5 events
papersTODAY 04:00 UTC

Paper Proposes Pareto-Optimal Offline RL Method for Multi-Objective LLM Alignment

A revised arXiv paper introduces a technique called smooth Tchebycheff scalarization for offline reinforcement learning, aimed at aligning large language models with human preferences using small labeled datasets. The authors focus on multi-objective alignment, where several preferences must be optimized at once rather than a single objective. The work appears in the cs.LG and cs.AI categories as a replacement submission.

llm-alignment/reinforcement-learning/research/multi-objective
papersTODAY 04:00 UTC

REGEN paper proposes replay-recycling for expert-to-generalist LLM distillation via offline RL

A revised arXiv paper introduces REGEN, a method that recycles replay data to distill specialized expert policies into a more general model using offline reinforcement learning. The approach targets the cost of scaling online RL, which is widely used to develop long-horizon reasoning and tool-use abilities in large language models. The v3 revision appears in both the cs.AI and cs.LG listings.

rl/distillation/llm-training/research
papersTODAY 04:00 UTC

Paper Proposes One-Step Flow Policy for Offline Reinforcement Learning

A new arXiv paper introduces a method for learning multimodal one-step flow policies from fixed offline datasets using value-weighted optimal transport. The approach targets offline reinforcement learning, where action distributions are often multimodal and existing flow policies are slow to sample. It appears in both cs.AI and cs.LG cross-listings.

reinforcement-learning/flow-models/optimal-transport/research
papersTODAY 04:00 UTC

Covariate balance tests proposed for hidden confounding in offline RL

A new paper examines how covariate balance diagnostics, a tool borrowed from causal inference, can reveal hidden confounding or model misspecification when offline reinforcement learning is used to recommend treatments. The author argues these checks help assess whether learned treatment policies rest on valid assumptions. The work targets researchers applying RL to clinical or policy decision data.

reinforcement-learning/causal-inference/offline-rl/research
papersSEP 10 04:00 UTC

New arXiv Paper Proposes Softmax-Based Method for Inferring Optimal RL Values Offline

A research paper on arXiv examines offline inference of the optimal value function in reinforcement learning. The authors derive new nuisance quantities as fixed points of a self-induced Bellman equation, approximating the maximum Bellman operator with its softmax counterpart. The work contributes theoretical tools for estimating optimal values from offline data.

reinforcement-learning/research/arxiv/theory