arXiv (All)
open access
RLVR is a Kernel, Not a Function: Statistical Inference for pass@$k$ Crossovers
Reinforcement learning with verifiable rewards (RLVR) often improves pass@1 while falling behind its base model at larger sampling budgets $k$, a crossover read as evidence that RLVR only sharpens existing capability. We identify two limits to this reading. First, a visible crossing need not be stat
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
- Same Reward, Different Skills: When Multimodal RL Learns to Look#1157179
- Libra: Efficient Resource Management for Agentic RL Post-Training#1011120
- RMB: Reward Model Boosting Mitigates Reward Hacking#1117933
- Learning to Steer, Steering to See: Unveiling the Geometry of RLVR in Large Language Models via Trainable Vectors#1119417
- PoEM: Predicting RL Outcomes from Existing Policies#1078297
Record · ID 1037230
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