arXiv (All)
open access
Low-Rank Adaptation for Critic Learning in Off-Policy Reinforcement Learning
Scaling critic capacity is a promising direction for improving off-policy reinforcement learning (RL). However, recent work shows that larger critics are prone to overfitting and instability in replay-based bootstrapped training. In this paper, we propose using Low-Rank Adaptation (LoRA) as a struct
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
- Official Review · ICLR 2025#787007
- RankQ: Offline-to-Online Reinforcement Learning via Self-Supervised Action Ranking#1050876
- Complementing reinforcement learning with SFT through logit averaging in the post training of LLMs#996906
- Model-based Bootstrap for Offline Policy Evaluation in Tabular Reinforcement Learning#1034150
- Model-based Bootstrap for Offline Policy Evaluation in Tabular Reinforcement Learning#978449
Record · ID 1491505
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