arXiv (All)
open access
Self-Confirming Superposition Traps in Reinforcement Learning
Reinforcement learning (RL) trains representations on data selected by the agent's policy, which then uses the resulting returns to guide its next choices. We show that this loop can sustain a lower-return policy even when representation fitting is globally optimal on those data. In a self-confirmin
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Learning Under Forgetting: Statistical Support-Selective Retention in Stochastic Training Dynamics#1143369
- One Step, One Lead: Mitigating Higher-Order Interference in Multi-Domain Reinforcement Learning via Cross-Step Control#974045
- Bellman Meets Lyapunov: Unsupervised Reinforcement Learning via Mastering Chaos#1161048
- Bellman Meets Lyapunov: Unsupervised Reinforcement Learning via Mastering Chaos#1157360
- Beyond Forgetting: Diagnosing and Harnessing Shared Reasoning in Continual RLVR#1077935
Record · ID 1117678
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive