arXiv (All)
open access
DART: Distributional Adversarial Recurrent Training for Algorithm Learning
Recurrent reasoning models (RRMs) can solve structured problems, achieving easy-to-hard generalization through iterative computation in hidden space. These models are typically trained with instance-level supervision, which becomes increasingly problematic as task difficulty grows: valid solutions o
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
- Streamlined Constraint Reasoning via CNN Pattern Recognition on Enumerated Solutions#204836
- Post-Training at the Edge of Detectability: A Game-Theoretic Approach to Fine-Tuning#1118668
- GFlowNets and variational inference#798523
- GFlowNets and variational inference#797110
- Post-Training Language Models for Gold-Medal Performance in Coding Competitions#971891
Record · ID 973600
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