Adam
PulseAugur coverage of Adam — every cluster mentioning Adam across labs, papers, and developer communities, ranked by signal.
4 day(s) with sentiment data
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Pion optimizer preserves spectrum for stable LLM training
Researchers have introduced Pion, a novel spectrum-preserving optimizer designed for training large language models. Unlike traditional additive optimizers like Adam, Pion utilizes orthogonal transformations to update w…
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New PowerStep optimizer halves memory use for large model training
Researchers have introduced PowerStep, a novel memory-efficient optimizer for training large neural networks. Unlike traditional adaptive optimizers like Adam that store gradient statistics, PowerStep achieves adaptivit…
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New research refines Adam optimizer's memory and noise dynamics
Two new research papers explore the nuances of the Adam optimizer, a popular tool in deep learning. The first paper proposes a "refresh rule" for Adam's momentum parameter, suggesting it should scale with training data …
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Quantum-inspired optimization tackles non-convex machine learning problems
Researchers have introduced a new framework called Quantum-Inspired Evolutionary Optimization (QIEO) to tackle complex non-convex optimization problems in machine learning. This approach uses a probabilistic representat…
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New principle optimizes AI model training by aligning gradients and updates
Researchers have introduced a new principle called Greedy Alignment for selecting and tuning optimizer hyperparameters in machine learning. This principle treats optimizers as causal filters that map gradients to update…
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New research derives advanced optimizers from evolutionary principles
Researchers have developed a new method to derive advanced optimization algorithms directly from evolutionary principles, unifying previously disparate views of evolution. This approach introduces Darwinian Lineage Simu…
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GONO optimizer adapts Adam's momentum using directional consistency for better convergence
Researchers have introduced the GONO framework, an optimization signal designed to improve deep learning training by addressing the decoupling of directional alignment and loss convergence. Unlike existing optimizers th…
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LLM training optimized by new Module-wise Learning Rate Scaling via SNR method
Researchers have developed a new method called Module-wise Learning Rate Scaling via SNR (MoLS) to address optimization challenges in large language models (LLMs). This technique estimates module-level signal-to-noise r…
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New rod flow model tracks Adam optimizer at edge of stability
Researchers have developed a new "rod flow" model to better understand how adaptive gradient optimization methods, like Adam, operate at the edge of stability. This model extends previous work on gradient descent to inc…
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Regent brings Git-like version control to AI agent activity
Two new open-source projects, re_gent and Adam, are aiming to provide version control and embeddable libraries for AI agents, respectively. Re_gent is presented as a Git-like system for managing AI agent development, wh…
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Anon optimizer offers tunable adaptivity, outperforming Adam and SGD on key tasks
Researchers have introduced Anon, a novel optimizer designed to bridge the performance gap between adaptive methods like Adam and non-adaptive methods like SGD. Anon features continuously tunable adaptivity, allowing it…
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New AdamO optimizer enhances stability and performance in offline RL
Researchers have introduced AdamO, a novel optimizer designed to enhance stability in offline reinforcement learning. This new optimizer addresses the issue of 'collapse,' where errors in temporal-difference updates can…
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FG^2-GDN enhances long-context understanding with adaptive learning rates
Researchers have introduced FG$^2$-GDN, a novel approach to enhance long-context understanding in neural networks. This method improves upon existing Gated Delta Networks by replacing a scalar learning rate with a chann…
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New theories explore how pre-training and sparse connectivity enhance deep learning generalization
Three new papers explore the theoretical underpinnings of generalization in deep learning. One paper identifies pre-training as a critical factor for weak-to-strong generalization, demonstrating its emergence through a …
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AI advances in CAD automation and chatbot regulation move forward in 2026
The U.S. Senate Judiciary Committee has advanced the GUARD Act, which would require identity verification for users of AI chatbots. This bipartisan measure aims to protect minors from unregulated AI interactions. Separa…
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AdamFusion launches AI copilot for Autodesk Fusion 360 CAD
Adam, an AI copilot for Autodesk Fusion 360, has been released, enabling users to control CAD operations through native agents. The tool integrates as an add-in for Fusion 360, with installation instructions provided fo…
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AdaMeZO optimizer cuts LLM fine-tuning memory needs with Adam-style estimates
Researchers have introduced AdaMeZO, a novel optimizer designed to make fine-tuning large language models more memory-efficient. Unlike traditional methods that require significant GPU memory for backpropagation, AdaMeZ…
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Mindstream founders detail their journey building an AI newsletter
Mindstream, an AI newsletter founded by Adam and Matt, is sharing its origin story. The co-founders left their jobs two years ago to pursue the venture full-time, aiming to simplify the complex AI landscape for readers.…
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New research shows immediate derivatives suffice for online recurrent adaptation
Researchers have developed a new method for online recurrent adaptation that significantly reduces computational requirements. Their approach, termed 'Immediate Derivatives Suffice,' eliminates the need for propagating …
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Researchers analyze Adam's tradeoffs and enhance SignSGD with hybrid switching strategy
Two new research papers explore advancements in optimization algorithms for machine learning. One paper provides a theoretical analysis of the Adam optimizer, detailing its performance under non-stationary objectives an…