arXiv (All)
open access
CodeScaler: Scaling Code LLM Training and Test-Time Inference via Reward Models
Reinforcement Learning from Verifiable Rewards (RLVR) has driven recent progress in code large language models by leveraging execution-based feedback from unit tests, but its scalability is fundamentally constrained by the availability and reliability of high-quality test cases. We propose CodeScale
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
- Information-Gain Rewards over Diversity-Pruned Tests: GT-Anchored Verifier Co-Training for Reliable Code Generation#1035174
- CodeMidas: Scaling Agentic Coding RL Environments from Code Itself#1036478
- CodeMidas: Scaling Agentic Coding RL Environments from Code Itself#1006880
- CodeSkill: Latent Skill Abstraction for Long-Horizon Code Agents#1117955
- An Imperfect Verifier is Good Enough: Learning with Noisy Rewards#1117363
Record · ID 1117327
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