arXiv (All)
open access
From Objectives to What Models Learn: A Landau Theory of Invariant Learning
Invariant-learning objectives pursue similar goals yet produce qualitatively different regularization paths, leaving unclear when shortcuts can be suppressed without damaging stable modes. Our starting point is simple: in the desired shortcut-suppressed regime, shortcut loading is small, so the obje
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
- Functional compatibility as a determinant of persistent neural learning#1141628
- Evolution imposes an inductive bias that alters and accelerates learning dynamics#261151
- Robust Adversarial Reinforcement Learning with Risk Sensitivity and Critic Consistency Regularization#1064211
- One Step, One Lead: Mitigating Higher-Order Interference in Multi-Domain Reinforcement Learning via Cross-Step Control#974045
- REFINEPPO: Learning Continuous Control Policies by Iterative Action Refinement#1035078
Record · ID 1118683
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