Within many crypto communities, the words democracy and decentralization are often used interchangeably. They appear to represent well-meaning concepts based on “the power of the people,” and some projects attempt to demonstrate fairness with them. When we hear them together, it becomes tempting to treat them as twins. We can say they do share a house and borrow each other’s clothes, but they’re not the same thing. It’s important to understand that each word emphasizes a different aspect […]

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Tasks, stability, architecture, and compute: Training more effective learned optimizers, and using them to train themselves was a paper from September, 2020, by Luke Metz and co. It is another step towards replacing hand-designed features with learned functions, this time the optimizer. This has been a three year journey for Luke; listen to him describe what he’s learned along the way and where are the pain points, from research to engineering.

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arXiv:2602.03980v1 Announce Type: new Abstract: Because language is creative, any reasonable language model must generalize, deciding what to say in novel contexts by using information from similar contexts. But what about contexts that are not novel but merely infrequent? In hierarchical regression, the model’s predictions for behavior in a context are affected by observations from other similar contexts to the extent that 1) the current context is infrequent and 2) different contexts behave similarly. This is called adaptive […]

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Rapid advances in artificial intelligence are increasing the rate and steepness of informational and economic gradients experienced by human systems, challenging traditional models of adaptation based on stable identities, static optimization, and long-term professional blueprints. This study proposes a unified dynamical framework connecting thermodynamic entropy, information-theoretic entropy, and a formally defined entropy of the self through a shared stochastic gradient-flow model. Drawing on Langevin dynamics and simulated annealing, physical relaxation, probabilistic learning, and human identity formation are treated […]

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arXiv:2509.26000v2 Announce Type: replace-cross Abstract: Asymmetric actor-critic methods are widely used in partially observable reinforcement learning, but typically assume full state observability to condition the critic during training, which is often unrealistic in practice. We introduce the informed asymmetric actor-critic framework, allowing the critic to be conditioned on arbitrary state-dependent privileged signals without requiring access to the full state. We show that any such privileged signal yields unbiased policy gradient estimates, substantially expanding the set of admissible privileged […]

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arXiv:2511.19566v2 Announce Type: replace-cross Abstract: Open weight models, which are ubiquitous, rarely provide access to their training data or loss function. This makes modifying such models for tasks such as pruning or unlearning, which are constrained by this unavailability, an active area of research. Existing techniques typically require gradients or ground-truth labels, rendering them infeasible in settings with limited computational resources. In this work, we investigate the fundamental question of identifying components that are critical to the model’s […]

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Standard reward models typically predict scalar scores that fail to capture the multifaceted nature of response quality in non-verifiable domains, such as creative writing or open-ended instruction following. To address this limitation, we propose Rubric-ARM, a framework that jointly optimizes a rubric generator and a judge using reinforcement learning from preference feedback. Unlike existing methods that rely on static rubrics or disjoint training pipelines, our approach treats rubric generation as a latent action learned to maximize judgment accuracy. […]

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Current multimodal large language model (MLLM)-based autonomous driving systems struggle with deep contextual understanding, fine-grained personalization, and transparent risk assessment in complex real-world scenarios. This paper introduces ConsciousDriver, a novel context-aware multimodal personalized autonomous driving system designed to address these limitations. Our system integrates a Context-Awareness Module for richer environmental understanding and a Dynamic Risk Adaptation Mechanism to flexibly modulate driving behaviors based on real-time user prompts and situational risks. Built upon an extended MLLM architecture, ConsciousDriver processes […]

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The failure wasn’t technical When you’re responsible for rolling out AI inside a company, it’s easy to focus on the visible parts. Models. Connectors. Retrieval. Permissions. Latency. That’s the work everyone sees. But that’s not why my rollout struggled. It didn’t fail because the system was broken. It failed because I was designing AI for teams instead of with them. At Nextdoor, our early Work AI rollout stalled even though the technology was solid. Accuracy was fine. Performance […]

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We’ve been experimenting with federated RL for heterogeneous robotic manipulation and ended up building a framework that separates representation federation from policy learning. Preprint is here. https://www.preprints.org/manuscript/202601.2257 I’d genuinely appreciate feedback on the design choices, especially around aggregation and stability. submitted by /u/EitherFox1242 [link] [comments]

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