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By avoiding weight updates entirely, Nullify serves as an efficient, plug-and-play inference-time intervention framework for LLM unlearning, and matches or surpasses established baselines in forget quality while achieving near-lossless preservation of model utility.
Proposed Potential-Guided Policy Optimization (PGPO) provides finer-grained step-level credit assignment, especially within failed trajectories, and shows strong overall performance relative to recent group-based RL methods.
SLIM (Self-supervised Latent Interaction Model), a compact 0.5B-parameter latent interaction policy, which matches or exceeds representative large-scale VLA and world-action-model baselines with fewer parameters, no additional embodied pretraining, lower inference latency, and substantially lower GPU memory usage.
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