{"slug": "laser-latent-space-adjoint-matching-for-support-constrained-entropy-regularized", "title": "LASER: Latent Space Adjoint Matching for Support-Constrained Entropy-Regularized Offline RL", "summary": "MIT researchers introduced LASER, an offline reinforcement learning algorithm that applies latent-space adjoint matching to achieve entropy-regularized latent-space RL with expressive flow policies while avoiding backpropagation through time. In experiments across 40 OGBench tasks with varying dataset qualities, LASER achieved state-of-the-art performance using fixed method-specific hyperparameters across all tasks, outperforming baselines that used task- and dataset-specific tuning. The work addresses policy collapse and critic artifact exploitation that arise when naively optimizing a latent policy learned via flow-matching behavior cloning.", "body_md": "arXiv:2610.08989v1 Announce Type: new \nAbstract: While offline reinforcement learning (RL) enables policy optimization from static datasets without costly online interaction, it remains bottlenecked by the risk of executing out-of-distribution (OOD) actions. Recent approaches mitigate this by learning a behavior-cloning policy through flow matching and then performing RL within its constrained latent space. However, naively optimizing the latent policy can easily cause the policy to collapse into a brittle mode or exploit sharp artifacts of the learned critic. In this work, we find that entropy regularization is essential in latent-space RL for addressing these challenges. We introduce LASER, a novel offline RL algorithm that applies latent-space adjoint matching to achieve entropy-regularized latent-space RL with expressive flow policies while avoiding backpropagation through time. Through comprehensive experiments on 40 challenging OGBench tasks with varying dataset qualities, we show that LASER achieves state-of-the-art performance. Notably, LASER uses fixed method-specific hyperparameters across all tasks and outperforms the evaluated baselines, including those with task- and dataset-specific tuning, which highlights the robust applicability of LASER. Project website: https://mit-realm.github.io/laser/.", "url": "https://wpnews.pro/news/laser-latent-space-adjoint-matching-for-support-constrained-entropy-regularized", "canonical_source": "https://arxiv.org/abs/2610.08989", "published_at": "2026-10-08 04:00:00+00:00", "updated_at": "2026-10-08 04:19:14.202975+00:00", "lang": "en", "topics": ["machine-learning", "artificial-intelligence", "ai-research"], "entities": ["MIT", "LASER", "OGBench"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/laser-latent-space-adjoint-matching-for-support-constrained-entropy-regularized", "markdown": "https://wpnews.pro/news/laser-latent-space-adjoint-matching-for-support-constrained-entropy-regularized.md", "text": "https://wpnews.pro/news/laser-latent-space-adjoint-matching-for-support-constrained-entropy-regularized.txt", "jsonld": "https://wpnews.pro/news/laser-latent-space-adjoint-matching-for-support-constrained-entropy-regularized.jsonld"}}