Summary
This post introduces an arXiv paper (2508.03417) by Ebenezer Gelo, Geraud Nangue Tasse, and Steven James on safe offline reinforcement learning. Standard approaches assume dense per-step cost annotations, but in practice supervisors only provide trajectory-level stopping feedback: a binary signal at the first unsafe transition, with no per-step attribution. The authors frame this as a temporal credit assignment problem and propose the Redistribution-based Cost Inference (RCI) framework, which converts sparse stopping feedback into dense per-step costs via return decomposition, then trains a constrained offline policy on the augmented dataset. They prove that return-equivalent redistributions preserve the feasible policy set and optimal Lagrangian in constrained MDPs, establishing that the transformation is theoretically lossless while yielding better-conditioned cost critic learning in practice. Experiments on highway driving and robotic manipulation show significantly reduced violation rates compared to sparse and classifier-based baselines, with robustness to heterogeneous dataset composition and label noise.
Paper Overview
Field: Machine Learning
Authors: Ebenezer Gelo, Geraud Nangue Tasse, Steven James
Published: 2026-08-13
arXiv: 2508.03417
Summary
Safe offline reinforcement learning typically assumes access to dense per-step cost annotations, but in practice supervisors only provide trajectory-level stopping feedback: a binary signal at the first unsafe transition, with no per-step attribution. The authors frame this as a temporal credit assignment problem and propose the Redistribution-based Cost Inference (RCI) framework, which converts sparse stopping feedback into dense per-step costs through return decomposition, then trains a constrained offline policy on the augmented dataset.
The authors prove that return-equivalent redistributions preserve the feasible policy set and the optimal Lagrangian in constrained MDPs (CMDPs), establishing that the transformation is theoretically lossless, while in practice producing better-conditioned cost critic learning.
Experiments on highway driving and robotic manipulation demonstrate significantly lower violation rates compared to sparse and classifier-based baselines, along with robustness to heterogeneous dataset composition and label noise.
Original Abstract
Safe offline RL typically assumes access to dense per-step cost annotations... (see full paper at arXiv:2508.03417)
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