… AI-generated summary This paper proposes a novel approach to address the challenge that pretrained VLA models often fail to effectively improve performance and reduce adaptation costs during standard supervised finetuning SFT . …
… Motivated by this limitation, we propose a simple and effective method to reduce the rigidity of the massive activation token. Our approach consistently improves LLM performance across multiple tasks, including instruction following and math reasoning, in both training free and fine tuning settings. …
… Our approach leverages a shared bidirectional transformer encoder to jointly represent text, entity type labels, and relation type labels, enabling zero-shot extraction of arbitrary entity and relation types specified at inference time. …
… In this paper, we introduce Entrocraft, a simple rejection-sampling approach that realizes user-customized entropy schedule by biasing the advantage distributions . …
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… By introducing this simple modification, our general framework recovers LoRA and DyLoRA only by changing P and ensures all sub-ranks embed the available gradient information efficiently. …
… The main takeaway: continual post-training should not only control how far an LLM moves, but whether each new update remains geometrically compatible with the evolving model state. 太强了~ nerding out on a detail, gcwm links layer-wise covariances with a gaussian wasserstein barycenter to build a shar… …
… Existing approaches handle only fragments of this in-the-wild setting: AutoML and transferability estimation select models from small predefined pools or require expensive per-model forward passes on the target dataset, while model routing presupposes a given candidate pool. …
… View arXiv page View PDF Add to collection Community We usually think of RL as the go-to tool for complex reasoning CoT , but this paper demonstrates it is also a highly effective approach to enhance the direct recall of parametric knowledge! …
… This is a fantastic approach! …