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🔥🔥🔥 [June 30, 2025] We show that selecting more uniformly distributed data increases the minimum pairwise distance, which provably reduces neural network approximation error—leading to better training efficiency beyond the NTK regime. Code and Paper are available at: https://github.com/SafeRL-Lab/data-uniformity and https://arxiv.org/pdf/2506.24120.
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🔥🔥🔥 [May 19, 2025] We released M4R, a benchmark for evaluating massive multimodal understanding and reasoning in open space. Code, dataset, and leaderboard are available at: https://github.com/SafeRL-Lab/AccidentBench
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🔥🔥🔥 [May 07, 2025] We released RLBenchNet, a systematic benchmarking suite for evaluating neural network architectures in reinforcement learning. Code is available at: https://github.com/SafeRL-Lab/BenchNetRL
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🔭 I’m currently working on safe and robust learning theory and its applications in robotics and foundation models.
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🌱 We organized a safe reinforcement learning workshop and seminars, the researchers and students who are interested in safe RL are welcome to join us! The recorded videos are available on YouTube's Safe RL Channel, please see the YouTube Channel, Safe RL Seminar Homepage or Safe RL Workshop Homepage.