arXiv: cs.AI preprintsInternational6 November 2026
Learning the Task or Learning the Verifier? Rethinking Criteria Exposure in Embodied Self-Improvement
This is an official announcement record
Firsthand records what arXiv: cs.AI preprints announced and links to the original. The wording below is theirs, not ours.
arXiv:2611.08318v1 Announce Type: new Abstract: Self-improving embodied agents are increasingly being developed and compared on robot simulation benchmarks. Several recent works expose the benchmark's success criteria to the learning agent, a practice we call Criteria Exposure (CE). These criteria, originally written to verify policies trained by imitation learning, often demand more than the corresponding language instructions, so a score from such a verifier may not faithfully reflect whether the instructed task was completed, especially for robot-control programs written by an agent. As emb
Read the official announcement
Opens arxiv.org
More from arXiv: cs.AI preprints
- Formalizing inverse problems in the age of AI: possibilities, pitfalls, and an invitation6 November 2026
- KDASO: Knowledge-Data Dual-Driven Automated Skill Optimization for Liability Adjudication Tasks6 November 2026
- BlockLock: Reproducible LLM Inference with Fixed Batch Geometry6 November 2026
- Resource-Efficient Reinforcement Learning Post-Training through Elastic Expert Parallelism6 November 2026
- The Two-Ledger Model: Transaction-Grain Cost Accounting for Multi-Agent AI Systems Across Clouds, Models, and Systems of Record6 November 2026
This content is for informational purposes only and is not professional advice. Specifications, prices, plan tiers, and features change frequently and may differ from what is shown here; verify current details on the manufacturer's or company's official page before purchasing. Ratings are based on analysis of published documentation, not independent lab testing.