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Research questionHow can reasoning models keep improving on open-ended agentic tasks as human supervision and reliable rewards recede?Mathematics and code often provide automatically verifiable outcomes, but open-ended agentic tasks lack comparably reliable rewards. As human oversight and curated experience become scarce, autonomous feedback and self-generated experience can introduce reward hacking, feedback drift, curriculum collapse, and environment errors.
AI
AI Agents
Alignment & Safety
Evaluation & Benchmarks
LLM Pretraining & Post-training
Machine Learning
Reasoning
Reinforcement Learning
Latest papersRecent research connected to this question, newest first.Scaling Large Reasoning Models beyond Human Supervision: A Path toward SuperintelligenceThe source analyzes a progression from human judgments to reusable or autonomous rewards, and from human-curated tasks toward self-generated curricula and environments. It discusses the associated risks and organizes evaluation around policy capability, feedback fidelity, and experience quality; it does not present a single deployed learning system or establish that fully autonomous supervision is reliable.research paper · Sep 1, 2026
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