[
    {
        "id": "osp-20259",
        "type": "article-journal",
        "title": "Loop-Free Inverse Reinforcement Learning via Sequential Value Recovery with Q-Score Matching",
        "author": [
            {
                "family": "Chen",
                "given": "Yang"
            },
            {
                "family": "Zhang",
                "given": "Yitan"
            },
            {
                "family": "Witbrock",
                "given": "Michael"
            },
            {
                "family": "Hu",
                "given": "Shuyue"
            }
        ],
        "URL": "https://omanscience.com/ar/articles/loop-free-inverse-reinforcement-learning-via-sequential-value-recovery-with-q-score-matching",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "Inverse Reinforcement Learning (IRL) aims to recover a reward function that explains expert demonstrations. Existing IRL methods typically rely on a bi-level optimization procedure that alternates between reward learning and policy optimization, leading to substantial computational burden and training instability. In this work, we introduce a different route that eliminates policy optimization entirely by leveraging diffusion policies. Our key insight is that a diffusion policy encodes the action-gradient structure of the optimal soft Q function, enabling reward learning to be cast as a sequence of value recovery problems, thereby allowing us to bypass reward-policy loops inherent in prior IRL methods. Specifically, our method proceeds in three stages: (I) recovering the optimal soft Q function via action-gradient matching and estimating the corresponding soft value function (LogSumExp of Q values) in a way inspired by Gumbel regression; (II) calibrating these soft values by inferring a state-dependent offset; (III) extracting the reward by enforcing Bellman consistency. This leads to Loop-Free Inverse Reinforcement Learning (LFIRL), a fully offline algorithm that operates in a simple, loop-free, and sequential manner. LFIRL is simple to implement and significantly improves training efficiency while maintaining strong reward recovery performance. Empirically, across Maze, Franka Kitchen, Adroit Hand Pen, and Push-T benchmarks, LFIRL achieves 2-3x speedup over the fastest baselines, while matching or surpassing state-of-the-art methods in reward recovery quality."
    }
]