arXiv (All)
open access
Adapter Thickets: Splitting an RLVR Budget Beats Concentrating It
Majority voting over sampled completions is the workhorse of test-time scaling, and reinforcement learning with verifiable rewards (RLVR) is the workhorse for making each completion better. The standard pipeline composes the two: train one policy with RLVR, then sample it many times and vote. We sho
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
- Safe Meta-Reinforcement Learning via Information Space Reachability#1109949
- Reinforcement Learning to Accelerate Primal-Dual Hybrid Gradient for Linear Programming#1156818
- Inference-Time Nash Alignment#1013677
- BOOKMARKS: Efficient Active Storyline Memory for Role-playing#1117413
- Fair machine learning under partial compliance#716808
Record · ID 1155926
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