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Forget about re-balancing losses – gradient geometry is the key to unlearning in LLMs without sacrificing retention.
PPO can be made sample-efficient and stable for long-horizon reasoning in LLMs by treating the problem as a sequence-level contextual bandit, sidestepping the need for computationally expensive multi-sampling.
Finally, a diffusion model lets you puppeteer multiple objects in a video with just text prompts, opening the door to complex scene editing.
