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 structural regularizer for critic learning. Our approach freezes randomly initialized base matrices and optimizes only the corresponding low-rank adapters, thereby constraining critic updates to a low-dimensional subspace. We evaluate our method across different off-policy RL algorithms, including SAC and FastTD3 based on different network architectures. Empirically, LoRA efficiently reduces critic l...
| Authors | Yuan Zhuang, Yuexin Bian, Sihong He et al. |
|---|---|
| Primary category | cs.LG |
| Published | 21 April 2026 |
| Last updated at source | 7 May 2026 |
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