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When OpenAI pulled the plug on its AI video app Sora in March, a landmark deal with Disney evaporated with it.  Under the three-year agreement, Sora would have generated short fan-created videos drawing on more than 200 characters from Disney, Marvel, Pixar, and Star Wars, with a curated selection streaming on Disney+. Disney had also committed to a $1 billion equity investment in OpenAI and was set to deploy ChatGPT enterprise-wide for its employees.  The agreement was supposed to […]

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I use AI tools every day. I think they’re genuinely brilliant. I hand Claude a disorganized pile of research notes and it finds connections I missed. I give it a rough thesis and it stress-tests it from angles I hadn’t considered. I’ve built entire websites, course curricula, and client deliverables with AI assistance. The technology is real, it’s powerful, and it has already changed how I work. But unless there is a serious breakthrough, AI is not ready […]

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Clinical Reasoning on Electronic Health Records (EHRs) is a fundamental yet challenging task in modern healthcare. While in-context learning (ICL) offers a promising inference-time adaptation paradigm for large language models (LLMs) in EHR reasoning, existing methods face three fundamental challenges: (1) Perspective Limitation, where data-driven similarity fails to align with LLM reasoning needs and model-driven signals are constrained by limited clinical competence; (2) Cohort Awareness, as demonstrations are selected independently without modeling population-level structure; and (3) Information Aggregation, […]

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One of the hardest parts of reinforcement learning isn’t the algorithm — it’s the reward function. You combine multiple objectives into a scalar reward, run training for hours, and the agent learns to optimize only one of them. Not because the others don’t matter, but because their gradients were too weak to compete. I built a tool to help catch this before training: Reward Design Workbench You define your reward components, set realistic state ranges, and the tool […]

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i know it might be a stupid question but what are your thoughts on specialised post-training becoming a narrower wedge over time? If the base models are already able to 80% agentic tasks out of box and ~15% can be covered by system prompt + few shot engineering, is specialised RL post-training worth the investment there? do companies like Prime-Intellect exist in that world? submitted by /u/Sharp_Variation7003 [link] [comments]

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Author(s): Yuval Mehta Originally published on Towards AI. Photo by Boitumelo on Unsplash No GPU cluster. No data team. No ML platform. Here’s what actually ships. Most ML content is written for teams that have things. A labelled dataset. An MLOps platform. A data engineer who answers Slack messages. A GPU budget that someone has already approved. You probably don’t have those things. You’re embedded in a product or analytics team, you were handed a vague mandate to […]

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arXiv:2604.05129v1 Announce Type: new Abstract: In this paper we investigate the exploitability of a Follow-the-Regularized-Leader (FTRL) learner with constant step size $eta$ in $ntimes m$ two-player zero-sum games played over $T$ rounds against a clairvoyant optimizer. In contrast with prior analysis, we show that exploitability is an inherent feature of the FTRL family, rather than an artifact of specific instantiations. First, for fixed optimizer, we establish a sweeping law of order $Omega(N/eta)$, proving that exploitation scales to the […]

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arXiv:2604.05125v1 Announce Type: new Abstract: Prior authorization (PA) requires interpretation of complex and fragmented coverage policies, yet existing retrieval-augmented systems rely on static top-$K$ strategies with fixed numbers of retrieved sections. Such fixed retrieval can be inefficient and gather irrelevant or insufficient information. We model policy retrieval for PA as a sequential decision-making problem, formulating adaptive retrieval as a Markov Decision Process (MDP). In our system, an agent iteratively selects policy chunks from a top-$K$ candidate set or […]

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