…It's novel, useful, and practical. CoT was a big leap for AI, sure, but ever since it became the standard, I've been looking for ways to escape it, because, despite…
…AI-generated summary Modern Large Language Models (LLMs) are often criticized for producing repetitive and homogeneous text, despite possessing vast latent vocabularies. While previous research has focused on model knowledge and training…
…Extensive experiments on challenging reasoning benchmarks demonstrate that TMAS achieves stronger iterative scaling than existing test-time scaling baselines, while hybrid reward training further improves scaling effectiveness and stability across iterations. Code…
…Extensive human evaluations show that Qwen-Image-2.0 substantially outperforms previous Qwen-Image models in both generation and editing, marking a step toward more general, reliable, and practical image generation foundation…
…We present Learning While Deploying (LWD), a fleet-scale offline-to-online reinforcement learning framework for continual post-training of generalist Vision-Language-Action (VLA) policies. Starting from a pretrained VLA policy…
…AI-generated summary Autoregressive video generation paradigms offer theoretical promise for long video synthesis, yet their practical deployment is hindered by the computational burden of sequential iterative denoising . While cache reuse strategies…
Papers arxiv:2606.08132 Phase Marginalization for Patch-Grid Instability in Vision Transformers Published on Jun 6 Submitted by Oğuzhan Ercan on Jun 9 BILGEM AI Authors: Oğuzhan Ercan Abstract Phase Marginalization…
…However, prior work on leveraging IG signals within the RL training loop faces three systematic challenges: normalizing across turns that face heterogeneous positional contexts can distort the relative standing of individual turns…
…Won't even share the code for us to train our own. Thanks for nothing, "Open" AI. Now we're stuck waiting for someone to abliterate it to even make it remotely…
…AI-generated summary Large Language Models (LLMs) are known to acquire reasoning capabilities through shared inference patterns in pre-training data, which are further elicited via Chain-of-Thought (CoT) practices. However…