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Weight updates leave readable textual imprints: decoding parameter shifts into natural language enables direct, gradient-based behavioral steering without any downstream fine-tuning data.
Purely autoregressive VLAs no longer require awkward hybrid diffusion heads to achieve fine-grained robot control: structuring discrete action tokenization as causally annealed flow matching recovers continuous diffusion precision while preserving end-to-end token efficiency.
