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Video diffusion models can now generate physically plausible 4D worlds thanks to a new pipeline that combines pretraining, supervised fine-tuning, and reinforcement learning.
Robots can now learn from their mistakes in real-time via a novel reflective planning framework, leading to significant performance gains in long-horizon tasks.
LLMs can be steered more effectively by viewing activation manipulation through the lens of ordinary differential equations and control theory, leading to significant gains in alignment benchmarks.
