Representation alignment (REPA) is widely used to accelerate the training of latent-space diffusion transformers (DiTs). However, applying it to pixel-space models such as Just Image Transformers (JiT) is challenging. Our analysis reveals a pronounced representation conflict between discriminative alignment and pixel-s...
Yi Tan, Bei-Er Zhu, Si-Rui Chen et al.· 0 citations
Recursive self-improvement (RSI) enables large language models to advance their capabilities through successive updates, but these updates can also introduce new risks and weaken existing safeguards. In line with the AI-45{\deg} Law, we pursue Safety RSI, in which safety develops as an intrinsic capability alongside ge...
Jing-Nan Zheng, Cheng Jin, Ming Zhang et al.· 0 citations
On-policy distillation (OPD) improves language-model post-training by letting the student learn from its own sampled trajectories under teacher supervision, aligning the training process with inference-time behavior. However, the rollout policy in OPD is usually treated as fixed: each round samples directly from the cu...
Si-Rui Chen, Yun-Zhe Qi, Hao-Bo Xu et al.· 0 citations
The routed-state interface unifies contextual memory and persistent capability adaptation within the model's native computation, reframing memory from a passive record of prior context into an active substrate for maintaining and evolving model behavior.
Ming Zhang, Kai-Sen Yang, Shu Yu et al.· 0 citations
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