arXiv:2601.09026v1 Announce Type: new Abstract: We present a new training methodology for transformers using a multilevel, layer-parallel approach. Through a neural ODE formulation of transformers, our application of a multilevel parallel-in-time algorithm for the forward and backpropagation phases of training achieves parallel acceleration over the layer dimension. This dramatically enhances parallel scalability as the network depth increases, which is particularly useful for increasingly large foundational models. However, achieving this introduces errors that cause systematic bias in the gradients, […]

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Pseudo-count is an effective anti-exploration method in offline reinforcement learning (RL) by counting state-action pairs and imposing a large penalty on rare or unseen state-action pair data. Existing anti-exploration methods count continuous state-action pairs by discretizing these data, but often suffer from the issues of dimension disaster and information loss in the discretization process, leading to efficiency and performance reduction, and even failure of policy learning. In this paper, a novel anti-exploration method based on Vector Quantized Variational […]

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When deploying a single predictor across multiple subpopulations, we propose a fundamentally different approach: interpreting group fairness as a bargaining problem among subpopulations. This game-theoretic perspective reveals that existing robust optimization methods such as minimizing worst-group loss or regret correspond to classical bargaining solutions and embody different fairness principles. We propose relative improvement, the ratio of actual risk reduction to potential reduction from a baseline predictor, which recovers the Kalai-Smorodinsky solution. Unlike absolute-scale methods that may not be […]

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Transformer-based language models are widespread in today’s society. As such, understanding the mechanisms by which they solve structured tasks and predicting how they may behave in novel scenarios is of great importance for safe deployment. We study the learning dynamics of attention heads in a controlled setting by training a decoder-only Transformer (GPT-J) on two structurally equivalent multi-hop reasoning tasks: a number task requiring positional reasoning and a letter task requiring symbolic reasoning. Using a recently introduced metric […]

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Hi, I am looking to do some project in robotics stimulation in the area of reinforcement learning. Can someone give me any good ideas as well as resources/platform to do so. I found one named Mojuco, but cannot find any good videos on that. submitted by /u/Southern_Reserve2609 [link] [comments]

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For those who aren’t aware, Mend Renovate (aka Renovate CLI aka Renovate) is an Open Source project for automating dependency updates across dozens of package managers and package ecosystems, 9 different platforms (GitHub, GitLab, Azure DevOps and more), and boasts support for tuning its behaviour to fit how you want dependency updates.

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arXiv:2603.08886v1 Announce Type: new Abstract: We study a class of finite-state channels, known as POST channels, in which the previous channel output serves as the current state. A POST channel is deemed approximately memoryless when the state-dependent transition matrices are sufficiently close to one another. For this family of channels, under a surjectivity condition on the associated memoryless reference channel, we show that the feedback capacity coincides with the non-feedback capacity. Consequently, for almost all approximately memoryless POST […]

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This article is based on my own experience deploying stablecoins across various DeFi lending markets. Over time, I noticed that headline APYs are often misleading unless you clearly understand what actually drives them. Here, I focus on simple lending markets and introduce a practical framework I personally use when evaluating where to deploy stablecoin liquidity. In subsequent articles, I plan to cover: yield-bearing stablecoins, working with principal tokens (using Pendle Finance as an example), AMM pools, and looping […]

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State space models (SSMs) have recently emerged for modeling long-range dependency in sequence data, with much simplified computational costs than modern alternatives, such as transformers. Advancing SMMs to graph structured data, especially for large graphs, is a significant challenge because SSMs are sequence models and the shear graph volumes make it very expensive to convert graphs as sequences for effective learning. In this paper, we propose COMBA to tackle large graph learning using state space models, with two […]

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