Redistribution-based Cost Inference Improves Sparse Safe Offline RL

arXiv:2608.12306v1 Announce Type: cross Abstract: Safe offline RL typically assumes access to dense per-step cost annotations, but in practice supervisors provide only trajectory-level stop-feedback: a binary signal at the first unsafe transition, with no per-step attribution. We frame this as a temporal credit assignment problem…

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Source: cs.AI updates on arXiv.org

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