[
    {
        "id": "osp-15466",
        "type": "article-journal",
        "title": "LASER: Latent Space Adjoint Matching for Support-Constrained Entropy-Regularized Offline RL",
        "author": [
            {
                "family": "Zhang",
                "given": "Songyuan"
            },
            {
                "family": "So",
                "given": "Oswin"
            },
            {
                "family": "Yu",
                "given": "Eric Yang"
            },
            {
                "family": "Cleaveland",
                "given": "Matthew"
            },
            {
                "family": "Crowley-Dolen",
                "given": "Peter"
            },
            {
                "family": "Fan",
                "given": "Chuchu"
            }
        ],
        "URL": "https://omanscience.com/ar/articles/laser-latent-space-adjoint-matching-for-support-constrained-entropy-regularized-offline-rl",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "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/."
    }
]