arXiv (All)
open access
LEEPS: Latent-Guided Explore-Exploit Prompt Sampling for Efficient RLVR in Large Language Models
Reinforcement learning with verifiable rewards (RLVR) improves the reasoning capabilities of large language models, but prompt groups with identical rollout rewards consume generation budget without effective learning signals. Pre-rollout prompt selection can reduce this waste by screening prompts b
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
- On the Pitfalls of Verbalized Confidence Priors for Calibrating Large Reasoning Models#1116801
- Apriel-Reasoner: RL Post-Training for General-Purpose and Efficient Reasoning#984141
- VLM-Guided Experience Replay#1117317
- Reasoning-Preserving Fine-Tuning of Post-RL LLMs with Null-Basis LoRA#1061255
- StraTA: Incentivizing Agentic Reinforcement Learning with Strategic Trajectory Abstraction#1105984
Record · ID 1011175
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