arXiv:2502.10510v3 Announce Type: replace-cross Abstract: Modern machine learning pipelines are increasingly combining and mixing data from diverse and disparate sources, e.g., pre-training large language models. Yet, finding the optimal data mixture is a challenging and open problem. We formalize this data mixing problem as a bi-level objective: the best mixture is the one that would lead to the best model for a downstream objective. Unfortunately, this objective is generally intractable. In this paper, we make the observation that […]

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arXiv:2602.16837v1 Announce Type: new Abstract: Transformer models systematically favor certain token positions, yet the architectural origins of this position bias remain poorly understood. Under causal masking at infinite depth, prior theoretical analyses of attention rollout predict an inevitable collapse of attention onto the first token. Such collapse, however, does not occur in practice. We resolve this discrepancy with a residual-aware theory of cumulative attention rollout. By incorporating residual connections, we show that this architectural component prevents collapse under […]

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arXiv:2502.11682v2 Announce Type: replace-cross Abstract: Strong Differential Privacy (DP) and Optimization guarantees are two desirable properties for a method in Federated Learning (FL). However, existing algorithms do not achieve both properties at once: they either have optimal DP guarantees but rely on restrictive assumptions such as bounded gradients/bounded data heterogeneity, or they ensure strong optimization performance but lack DP guarantees. To address this gap in the literature, we propose and analyze a new method called Clip21-SGD2M based on […]

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arXiv:2602.23370v1 Announce Type: new Abstract: Long-document topic segmentation plays an important role in information retrieval and document understanding, yet existing methods still show clear shortcomings in ultra-long text settings. Traditional discriminative models are constrained by fixed windows and cannot model document-level semantics; generative large language models can output paragraph boundaries, but inference is expensive and long inputs are difficult to support. To address these issues, we propose a discriminative segmentation model based on Qwen3-0.6B. On top of the […]

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:::info Astounding Stories of Super-Science April 2004, by Astounding Stories is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book here. THE COUNTRY OF THE BLIND – VII. — THE REMARKABLE CASE OF DAVIDSON’S EYES. Astounding Stories of Super-Science April 2004: THE COUNTRY OF THE BLIND – VII. — THE REMARKABLE CASE OF DAVIDSON’S EYES. By H. G. Wells ::: I. — The transitory mental aberration of Sidney Davidson, remarkable enough in […]

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We live in what are described as post-capitalist times, where the economic system that promotes the virtues of creating individual wealth has been variously described as broken, defunct, and even failed. It has, according to many, morphed into a system where the oligarchs control the resources required to make immense wealth and leave the rest to fend for themselves and fight over scraps. Some go to the extent of romanticizing the concept of a welfare state, where basic […]

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I’ve been trying to get to know more about self-improving agents and I’m specifically interested in systems where the agent detects it has failed, for instance with things like wrong tool calls, bad retrieval, hallucination, couldn’t find the right answer, and then automatically adjusts its own prompts or strategy so the same class of failure doesn’t happen again. I don’t talk about weight updates, but the prompt/instructions/orchestration logic evolving based on observed errors. I’m aware of work in […]

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arXiv:2602.09203v1 Announce Type: new Abstract: Robot morphology, the form, shape, and structure of robots, is a key design space in human-robot interaction (HRI), shaping how robots function, express themselves, and interact with people. Yet, despite its importance, little is known about how design frameworks can guide systematic form exploration. To address this gap, we introduce Elements of Robot Morphology, a framework that identifies five fundamental elements: perception, articulation, end effectors, locomotion, and structure. Derived from an analysis of […]

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Reinforcement learning has become the central approach for language models (LMs) to learn from environmental reward or feedback. In practice, the environmental feedback is usually sparse and delayed. Learning from such signals is challenging, as LMs must implicitly infer how observed failures should translate into behavioral changes for future iterations. We introduce Experiential Reinforcement Learning (ERL), a training paradigm that embeds an explicit experience-reflection-consolidation loop into the reinforcement learning process. Given a task, the model generates an initial […]

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Hey folks, My team and I are diving into RL training setups and want to chat with folks who have hands-on experience. Could share your process for picking an environment (e.g., Gym, custom sims) and getting it up and running? What pain points have you hit—like scaling, reward shaping, or integration issues—and what fixes made life easier? DMs open or reply below—happy to hop on a quick call! Thanks! submitted by /u/Popular_Piglet_1443 [link] [comments]

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