---
title: "Machine Learning"
description: "622 posts about Machine Learning, summarised, each linking to the original."
---

# Machine Learning
> 622 posts about Machine Learning, summarised, each linking to the original.

## Articles

### [Same Cluster, 33 Points More Utilization: What Changed Was the Order](https://yomu.fyi/post/same-cluster-33-points-more-utilization-what-changed-was-the-order.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Gabriel Pimenta de Freitas Cardoso, Breno de Almeida Beleza, Francisco de Almeida Rocha Alves, Bruno Duarte
- Published: Aug 17, 2026
- Type: Benchmark

The post presents a constraint-aware GPU allocator as a response to utilization losses caused by fixed real-time reservations and FIFO ordering under contention. It compares the allocator with FIFO on identical hardware and workloads across seven benchmark scenarios, modeling batch-like jobs as contiguous GPU blocks and real-time inference as a timestep-by-timestep demand curve with bounded GPU swaps. Across five contention scenarios, utilization improved from 52–85% to 72–88%, while priority-weighted value increased 24.6–105.1%; the strongest case gained 33 percentage points of utilization and 105% value, and the scale test gained 15.9% value at identical utilization and throughput. The scheduler optimizes a 24-hour horizon but commits only the current timestep, re-running every 30–60 minutes so fresh observations absorb forecast error while preserving running work.


### [Developing Nemotron 3.5 Lightning NVFP4 with QAD Using NVIDIA Model Optimizer](https://yomu.fyi/post/developing-nemotron-3-5-lightning-nvfp4-with-qad-using-nvidia-model-op.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Tanya Lenz
- Published: Aug 17, 2026
- Type: Tutorial

NVIDIA’s post presents a workflow for developing an NVFP4 checkpoint of the Nemotron 3.5 Lightning 30B-A3B model, targeting lower memory use and higher inference throughput without sacrificing accuracy. The method starts with post-training quantization (PTQ), producing a W4A16 student from the BF16 teacher, then applies quantization-aware distillation (QAD), where simulated quantization and KL-divergence loss align student logits with the frozen teacher. For the selected four\_over\_six recipe, 32K calibration performed best, and the recipe using W4A16 Mamba linears offered the best reported trade-off between accuracy degradation and inference performance; the checkpoint is 22 GB versus 66 GB and delivers up to 4x faster throughput. The workflow includes Model Optimizer examples and launchers for Megatron-LM and Megatron-Bridge, covering calibration, distillation, evaluation, and export.


### [Using AI\_Functions in Your Data Warehouse: Top Use Cases](https://yomu.fyi/post/using-ai-functions-in-your-data-warehouse-top-use-cases.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Srikant Das, Ismail Makhlouf
- Published: Aug 14, 2026
- Type: Tutorial

Databricks AI Functions bring model inference into SQL queries, addressing the need to combine warehouse-resident structured data with unstructured reviews, tickets, PDFs, and transcripts without exporting rows to a separate service. The post presents six Lakehouse use cases: document parsing and extraction, sentiment classification, translation, ticket routing, sales-call extraction, and generative drafting with ai\_query. Task-specific functions such as ai\_parse\_document, ai\_extract, ai\_classify, and ai\_translate handle specialized work, while ai\_query sends prompts to an accessible Databricks-hosted Foundation Model serving endpoint. Unity Catalog permissions, existing pipelines, and system.billing.usage keep governance and billing within Databricks, while SQL execution manages planning, parallelization, and retries. Production guidance includes tagging jobs, sampling at least 10,000 rows, choosing models deliberately, requesting structured output, and versioning prompts.


### [State of Open Models: Summer 2026 Observations](https://yomu.fyi/post/state-of-open-models-summer-2026-observations.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Adina Yakefu, Apolinário from multimodal AI art, Irene Solaiman
- Published: Aug 14, 2026
- Type: Benchmark

The report analyzes Hugging Face Hub activity from January through August 2026 to assess how open-model releases, usage, licensing, and agent traffic are changing. Public repositories expanded from 2.43 to 2.96 million models, while 85.6% of models had fewer than 200 lifetime downloads and 1.5% of repositories captured 99.2% of downloads. Chinese labs reached monthly ceilings of 754B to 2.78T parameters, while broader portfolios such as Qwen’s generated far more downloads than frontier-only strategies; likes and downloads measured attention and infrastructure dependence differently. Large Chinese releases were often Apache 2.0 or MIT licensed, though newer trillion-parameter models introduced restrictions, while agents became the Hub’s leading user and autonomous-agent activity exposed the practical value of running quantized open models locally.


### [When Can LLMs Replace Humans in A/B Tests?](https://yomu.fyi/post/when-can-llms-replace-humans-in-a-b-tests.md)
- Company: [Spotify](https://yomu.fyi/company/spotify.md)
- Author: Spotify Engineering
- Published: Aug 13, 2026
- Type: Explainer

Replacing human participants in A/B testing with large language model predictions eliminates causal identification guarantees by design, forcing reliance on surrogate endpoint assumptions. An empirical evaluation using the Upworthy dataset revealed that raw gpt-4o-mini click-through rate predictions exhibited systematic attenuation bias, capturing only 39 percent of observed human treatment effects. To address this discrepancy, researchers formalized surrogacy and comparability conditions, finding that nonlinear machine learning calibrators like random forests and gradient-boosted trees successfully recovered treatment effects where linear ordinary least squares failed. In addition, averaging multiple sample draws per experimental unit mitigated variance and zero-bias caused by model generation temperature. Ultimately, because comparability cannot be verified for novel interventions that depart significantly from historical experiments, calibrated models cannot entirely replace human testing for genuine product innovations.


### [Record, train, and deploy from one place with Strands Agents, LeRobot, and Hugging Face Storage Buckets](https://yomu.fyi/post/record-train-and-deploy-from-one-place-with-strands-agents-lerobot-and.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Sundar Raghavan, Steven Palma, Cagatay Cali, Arron Bailiss, Yin Song
- Published: Aug 13, 2026
- Type: Tutorial

This walkthrough presents a streaming data loop for Strands Robots that records robot demonstrations, stores them in Hugging Face Storage Buckets, trains from the growing dataset, and deploys a resulting policy back to hardware. It uses the LeRobot on-disk format throughout: Robot("so100") records through DatasetRecorder, sync\_dataset\_to\_bucket writes changed bytes to a mutable bucket, and stream\_dataset reads the dataset from the Hub without a full local download while decoding camera video on the fly. The agent can decide which episodes to keep and when to retrain, while the same Robot object records and streams data; deployment changes the robot mode keyword from simulation to real. The default simulation path runs with a mock policy that produces a valid dataset but not a useful one, while hardware, bucket, and training paths require additional dependencies.


### [Why Enterprise Intelligence Needs Purpose-Built AI](https://yomu.fyi/post/why-enterprise-intelligence-needs-purpose-built-ai.md)
- Company: [Brandwatch](https://yomu.fyi/company/brandwatch.md)
- Author: Ksenia Newton
- Published: Aug 13, 2026
- Type: Explainer

Enterprise intelligence requires more than the plausible answers produced by general-purpose LLMs: it needs accurate insights from vast real-world brand data, supported by statistics, governance, and domain expertise. LLMs can hallucinate, miss context, produce inconsistent answers, and rely on limited, non-randomly sampled sources, making them unsuitable as the sole basis for high-stakes decisions. The article argues for matching technologies to tasks, using statistical models for quantitative problems such as social and online-news Reach, specialized classifiers for other tasks, and generative AI to synthesize model outputs. At enterprise scale, crisis and reputation use cases also demand near-real-time speed, consistency, predictability, explainability, auditability, monitoring, and bias testing. LLMs still have a role, including as orchestrators grounded in high-quality data, workflows, specialized models, and rigorous evaluation; the proposed future combines varied models rather than choosing between LLMs and traditional AI.


### [What We Learned by Reproducing 2,200 papers from ICML](https://yomu.fyi/post/what-we-learned-by-reproducing-2-200-papers-from-icml.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Abubakar Abid
- Published: Aug 13, 2026
- Type: Benchmark

An ICML 2026 reproduction hackathon examined whether coding agents could audit research at conference scale, amid 23,918 submissions and 6,352 accepted papers. From July 15 to August 2, 1,221 participants published 6,816 Trackio logbooks covering 2,226 papers and 35,908 claims; an open-weights GLM-5.2 judge assigned per-claim verdicts, while participants used cloud jobs and toy reproductions when full experiments were impossible. At least one claim was independently verified in 51% of examined papers, including 266 fully reproduced papers, while 23% had a falsified or contested claim and 242 papers produced opposing verdicts across teams. Adversarial re-checks confirmed issues including a paging-theorem error, a reverse-versus-forward-KL mismatch, and padded evaluation tokens, but also caught false falsifications, leading to the conclusion that agents expand auditing capacity while humans remain necessary to steer experiments and assess cases such as image quality.


### [Introducing OlmoEarth embeddings: Custom embedding exports from OlmoEarth Studio for downstream analysis](https://yomu.fyi/post/introducing-olmoearth-embeddings-custom-embedding-exports-from-olmoear.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Kyle Wiggers
- Published: Aug 12, 2026
- Type: Announcement

OlmoEarth Studio now supports custom computation and export of embedding vectors, compact representations of Earth-observation data generated by open-source OlmoEarth foundation models. Users can select an area, a one-to-twelve-month time span, an encoder variant, spatial resolution, and Sentinel-2 L2A or Sentinel-1 RTC imagery, then receive a Cloud-Optimized GeoTIFF with one int8 band per embedding dimension. The post demonstrates similarity search, few-shot segmentation, change detection, and PCA-based exploration; a logistic regression trained on 60 labeled pixels produced a weighted F1 of 0.84 for three land-cover classes in Ca Mau, Vietnam. Embeddings provide a fast, shareable starting point, while supervised fine-tuning can improve performance for applications needing task-specific results; cloud cover, atmospheric artifacts, and missing observations can affect quality.


### [How We’re Building Scam Alert on WhatsApp With End-to-End Encryption and Verifiability Guarantees](https://yomu.fyi/post/how-we-re-building-scam-alert-on-whatsapp-with-end-to-end-encryption-a.md)
- Company: [Meta](https://yomu.fyi/company/meta.md)
- Published: Aug 12, 2026
- Type: Announcement

WhatsApp presents an early technical overview of Scam Alert, an optional feature that uses an on-device machine learning model to identify potential scam messages from non-contacts while keeping message content on the device. The warning appears only to the user, who can block, report, continue, or mark a chat trusted; reporting and sharing require explicit user action. To measure performance, the system sends locally aggregated warning and user action counts through confidential federated analytics using Trusted Execution Environments and differential privacy, rather than transmitting raw signals. Model versions are recorded on a public transparency ledger, while published weights, client-side logs, and an expanded Bug Bounty program support independent inspection of delivery and behavior.


### [NVIDIA Nemotron 3.5 Lightning Delivers Fast, Accurate Specialized Task Execution for Long-Running Agents](https://yomu.fyi/post/nvidia-nemotron-3-5-lightning-delivers-fast-accurate-specialized-task.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Tanya Lenz
- Published: Aug 11, 2026
- Type: Announcement

NVIDIA Nemotron 3.5 Lightning is an open 30B mixture-of-experts model with 3B active parameters, designed for the high-volume execution layer of long-running autonomous agents. The post positions it alongside frontier reasoning models: larger systems handle planning and orchestration, while Lightning handles tool calls, result validation, subagent delegation, and other routine work. Its design combines harness-optimized training, multi-token prediction for speculative decoding, DFlash and DSpark draft models, and NVFP4 quantization alongside BF16. The model is customizable through LoRA or full SFT, reinforcement learning, and environment-based evaluations, with weights, training data, and recipes released under OpenMDW-1.1. The post reports up to 4x the output speed of similar-sized models, 86% accuracy on PinchBench, and completion of 10,000 tasks 30% faster than Qwen3.6 35B at similar accuracy, while NeMo Switchyard routes work among models.


### [AWS Trainium Frontier competition: Co-design models and kernels on purpose-built AI chips](https://yomu.fyi/post/aws-trainium-frontier-competition-co-design-models-and-kernels-on-purp.md)
- Company: amazon.science
- Author: Louise Ping, John Gray, Emily Webber, Josh Longenecker
- Published: Aug 10, 2026
- Type: Announcement

Modern language model architectures evolved around specific hardware constraints, but AWS Trainium chips alter memory and compute dynamics through on-chip SBUF memory, systolic matrix multiplication, and explicit DMA control. The AWS Trainium Frontier competition invites teams to train language models from scratch under strict wall-clock time constraints to uncover optimal architectures and custom kernels for Trainium silicon. Participants start from a fifty-million-parameter dense baseline and can modify everything from the model architecture and optimizer to custom Neuron Kernel Interface routines. Phase 1 evaluates thirty-minute single-chip training runs by validation bits-per-byte, while Phase 2 grants four-hour multi-chip server access to evaluate both training efficiency and reasoning on the CORE benchmark. Top finalists will present their findings at NeurIPS 2026, compete for cash prizes, and co-publish research with Annapurna Labs.


### [Build Low-Latency Multilingual Voice Agents: Open Weights & Full Deployment Control with NVIDIA Magpie TTS](https://yomu.fyi/post/build-low-latency-multilingual-voice-agents-open-weights-full-deployme.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Maryam Motamedi, Mikyas Desta, Jason Li, Jason Roche
- Published: Aug 10, 2026
- Type: Announcement

NVIDIA Magpie Multilingual TTS is a 364M-parameter open-weights model for multilingual voice applications, supporting 12 languages, including newly added Modern Standard Arabic, Korean, and Brazilian Portuguese. It is positioned for cascaded voice-agent architectures in which ASR, TTS, and LLM components are independently deployed, tuned, and scaled on controlled infrastructure. Frame stacking predicts two audio frames per decoding step, while a local transformer models dependencies between simultaneously generated codebook tokens; NVIDIA reports 32–79 ms single-stream TTFA across its listed GPUs. Compared with the previous release, French and Spanish CER improved to 1.54% and 0.60%, while SSIM reached 0.747 and 0.793; German SSIM also increased to 0.742. The Hugging Face checkpoint supports research and fine-tuning, while NVIDIA NIM provides optimized production serving and NeMo supports customization.


### [Making Knowledge Distillation Cheap Enough to Run at Scale](https://yomu.fyi/post/making-knowledge-distillation-cheap-enough-to-run-at-scale.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Antonio Tiene, Iker García-Ferrero, Ali Hashemi, Bakbergen Ryskulov
- Published: Aug 10, 2026
- Type: Problem & solution

Knowledge distillation trains a smaller student to match a larger teacher's capabilities, but conventional online training keeps both models loaded and materializes full vocabulary distributions at every step, making long-context recovery expensive. The proposed system caches the teacher's top-100 logits once and introduces a fused, chunked KL-divergence loss that avoids storing the full vocabulary-by-sequence grid or the student's full logits. In benchmarks with an 8K-token context on one H200, offline fused chunked KL used 58.3GB peak memory versus 102.8GB for online training, while all four methods reached near-identical training loss. At 32K tokens, the fully chunked loss reduced isolated kernel memory from 85.2 GiB to 5.45 GiB; distilling GPT-OSS 20B at that context cut step time from 57.0 to 12.23 seconds and reduced the setup from four GPU nodes to one.


### [Unveiling good and bad behaviors on the Agentic Internet](https://yomu.fyi/post/unveiling-good-and-bad-behaviors-on-the-agentic-internet.md)
- Company: [Cloudflare](https://yomu.fyi/company/cloudflare.md)
- Author: Jin-Hee Lee
- Published: Aug 7, 2026
- Type: Explainer

Web traffic increasingly shifts between human interaction and automated agents, rendering point-in-time security checks ineffective against sophisticated automation. Cloudflare addresses this shift through continuous behavioral analysis designed to evaluate trust across entire user sessions rather than relying on static point-in-time hurdles. Its CDN-injected client-side system, Precursor, tracks continuous behavioral indicators like cursor movements to catch mid-session automated takeovers. To counter rapid bot adaptation, Cloudflare is introducing self-adjusting machine learning models under Adaptive Intelligence and non-deterministic defenses such as the AI Labyrinth. These tools allow site owners to poison scraper training data, trap crawlers in synthetic webs, or queue legitimate agents while dynamically penalizing abusive traffic.


### [From User Sequences to Scaling Laws: A Multi-Stage Architecture for Meta’s Ads Ranking](https://yomu.fyi/post/from-user-sequences-to-scaling-laws-a-multi-stage-architecture-for-met.md)
- Company: [Meta](https://yomu.fyi/company/meta.md)
- Author: By Steven De Gryze, Parshva Doshi, Sean O'Byrne, Arnold Overwijk, Dinesh Ramasamy, Lee Xiong
- Published: Aug 5, 2026
- Type: Explainer

Meta’s ads ranking systems process millions of candidates per second under millisecond latency budgets, creating tension between richer sequence models and efficient serving. The post presents a multi-stage architecture that moves long-history user modeling into asynchronous offline transformers, caches user-level embeddings, and combines them with fresh user and ad signals in an online ranking model. Dense tokenization places sparse features and behavioral sequences in one vocabulary, while target-aware multi-head attention learns higher-order interactions between historical behavior and the candidate ad. On real-world ads traffic, performance follows a log-linear relationship with compute across depth, enrichment, width, and sequence length, with balanced growth and diverse sequences identified as scaling levers. Together with broader model innovations, the approach contributed to a cumulative 6% conversion lift on Instagram, 3% on Facebook, and 3.5% lift in Facebook ad clicks, and serves as a core component of GEM.


### [34 Amazon Research Awards Build on Trainium recipients announced](https://yomu.fyi/post/34-amazon-research-awards-build-on-trainium-recipients-announced.md)
- Company: amazon.science
- Author: Amazon Research Awards team
- Published: Aug 5, 2026
- Type: Announcement

Amazon announced 34 recipients from 30 universities for the Fall 2025 Build on Trainium: Responsible AI call for proposals. Part of a 110 million dollar credit initiative, the program funds academic research across new model architectures, machine learning libraries, optimizations, and distributed systems running on AWS Trainium hardware. The funded projects center on five key areas: AI safety and alignment, multilingual language models, representation engineering, sustainability with small language models, and deep learning for synthetic data generation. Awardees receive AWS Promotional Credits, access to more than 700 Amazon public datasets, assigned Amazon research contacts, and dedicated technical training resources. Highlighted efforts include parallelization strategies for trillion-parameter mixture-of-experts models scaling across 1,024 Trainium chips and inference optimization frameworks designed to enhance token efficiency.


### [Sidekick's continual learning loop](https://yomu.fyi/post/sidekick-s-continual-learning-loop.md)
- Company: [Shopify](https://yomu.fyi/company/shopify.md)
- Author: 2023-10-18
- Published: Aug 5, 2026
- Type: Explainer

Frontier models can accelerate an AI product launch, but their serving cost and latency can become impractical as usage grows, while their weights do not learn from production failures. Shopify’s proposed flywheel captures low-quality conversations, defines quality with a rubric and calibrated judges, improves the surrounding harness through autoresearch, and then converts repaired trajectories into supervised fine-tuning and GRPO training signal. A panel of frontier reasoning models critiques failures, an arbiter produces repair instructions, and unresolved cases go to expert annotators; gist compression then shortens the agent’s static prompt without measured judge-quality loss. In production, the GraphQL agent serves up to 2,000 requests per minute and reportedly achieves a 96% serving-cost reduction versus an estimated frontier-model cost, with lower latency and higher throughput. The approach is presented as a continual-learning system that turns production experience into model-weight updates.


### [Beyond VLAs: How World Action Models Reshape Robot Manipulation](https://yomu.fyi/post/beyond-vlas-how-world-action-models-reshape-robot-manipulation.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Michelle Horton
- Published: Aug 4, 2026
- Type: Explainer

Robot policies built on vision-language-action models often struggle with physical generalization because vision-language backbones describe scenes instead of predicting physical dynamics. World action models address this limitation by building policies on video world models that jointly predict actions and future world states. NVIDIA Cosmos 3 provides an open foundation using a Mixture-of-Transformers architecture where an autoregressive transformer guides a diffusion transformer to synthesize continuous modalities including video and robot actions. Specializing Cosmos 3 on the DROID platform produced policies capable of simultaneously outputting action trajectories and predicted video observations while boosting RoboLab success from 28.1% to 36.8%. These models support flexible deployment tiers, from workstation serving on RTX PRO 6000 GPUs to real-time 15 Hz on-device execution on NVIDIA Jetson Thor embedded hardware.


### [Generate Trajectories, Reasoning Traces, and Auto-Labels with NVIDIA Alpamayo 2 Super](https://yomu.fyi/post/generate-trajectories-reasoning-traces-and-auto-labels-with-nvidia-alp.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Elizabeth Goodman
- Published: Aug 4, 2026
- Type: Announcement

Autonomous vehicle development typically relies on disjoint models for trajectory generation, intent prediction, scene understanding, and data labeling. NVIDIA Alpamayo 2 Super addresses this fragmentation with an open 34-billion-parameter vision-language-action model that couples a 32-billion-parameter Cosmos 3 Super Reasoner with a 2-billion-parameter diffusion-based Action Expert. Processing surround-view video across up to seven cameras, the model simultaneously produces future vehicle trajectories, Chain-of-Causation reasoning traces, high-level meta-actions, and grounded scene question answers. In benchmark evaluations, the model achieved a 6.4-second minADE\_6 of 0.911 meters on the Physical AI AV Dataset and ranked first on LingoQA with a score of 79.2. Released under the OpenMDW-1.1 permissive license, the architecture serves as an offline policy teacher, evaluation critic, or auto-labeling engine that can be distilled for NVIDIA DRIVE AGX Thor deployment.


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