---
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

### [Unlocking Agentic RL Training for GPT-OSS: A Practical Retrospective](https://yomu.fyi/post/unlocking-agentic-rl-training-for-gpt-oss-a-practical-retrospective.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Jason Zhu, Hejian Sang, Arup De, Rohit Jain, Yanning Chen
- Published: Jan 27, 2026
- Type: Problem & solution

Engineers evaluated the GPT-OSS model for agentic reinforcement learning across multi-step tasks such as ReTool and GSM8K using the verl framework. Initial training runs suffered from exploding gradient norms, increasing KL divergence, and stagnating rewards. Investigation revealed that non-deterministic expert routing in the Mixture of Experts architecture caused log-probability mismatches between forward passes, which falsely triggered Proximal Policy Optimization clipping. The team resolved this by substituting detached current log-probabilities for old log-probabilities during on-policy updates. To address further training instabilities and memory bottlenecks, the implementation integrated an attention-sink-aware backward pass into FlashAttention v3 and applied sequence parallelism using all-to-all communication around attention layers.


### [AI in Investment Management: 2026 Outlook (Part II)](https://yomu.fyi/post/ai-in-investment-management-2026-outlook-part-ii.md)
- Company: [Two Sigma](https://yomu.fyi/company/two-sigma.md)
- Author: Emily Majewski
- Published: Jan 21, 2026
- Type: Explainer

Two Sigma leadership outlines the evolving role of artificial intelligence across quantitative investing workflows heading into 2026. The firm is embedding frontier large language models into internal systems, incident management, and feature generation pipelines to accelerate research tasks that previously took months into days. Technical focus across the broader field is shifting from raw parameter scaling toward efficiency optimizations, multimodal unified representations, and mechanistic interpretability circuits. In forecasting pipelines, rapid automated hypothesis generation introduces severe risks of overfitting and compromised backtesting, particularly when pre-trained models already contain historical regime knowledge prior to their cutoff dates. Consequently, engineering success requires strong institutional research discipline, production monitoring, and skepticism alongside the adoption of automated agentic tooling.


### [AssetOpsBench: Bridging the Gap Between AI Agent Benchmarks and Industrial Reality](https://yomu.fyi/post/assetopsbench-bridging-the-gap-between-ai-agent-benchmarks-and-industr.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Dhaval Patel, James Rayfield, Saumya Ahuja, Chathurangi Shyalika, Shuxin Lin, Zhou, Ayhan Sebin
- Published: Jan 21, 2026
- Type: Benchmark

AssetOpsBench is an evaluation framework designed to benchmark agentic AI systems within industrial Asset Lifecycle Management operations, such as chillers and air handling units. The benchmark incorporates 2.3 million sensor telemetry points, 4,200 work orders, 53 structured failure modes, and over 140 curated scenarios spanning four agent roles. Systems are assessed across six qualitative criteria, including task completion, retrieval accuracy, result verification, sequence correctness, clarity and justification, and hallucination rate. Evaluation traces are analyzed using a trajectory-level diagnostic pipeline called TrajFM, which extracts and clusters failure patterns. In community tests covering over 300 agents, no evaluated model family reached the 85-point readiness threshold.


### [One Year Since the “DeepSeek Moment”](https://yomu.fyi/post/one-year-since-the-deepseek-moment.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Adina Yakefu, Irene Solaiman
- Published: Jan 20, 2026
- Type: Explainer

The January 2025 release of DeepSeek's R1 model transformed China's artificial intelligence sector from a closed ecosystem into an organic open-source ecosystem. By releasing under the MIT license and sharing reasoning paths and post-training methods, DeepSeek turned complex reasoning into a reusable, downloadable engineering asset suitable for distillation in compute-constrained environments. Chinese technology organizations subsequently shifted from comparing standalone benchmark scores to developing ecosystem interfaces, application scenarios, and system-level infrastructure. Major players rapidly expanded their open footprints, driving downloads of newly created Chinese models on Hugging Face past those of any other nation. This momentum catalyzed global open-source adoption across international markets while prompting Western organizations to develop competing open-weight alternatives or build directly upon open Chinese foundations.


### [Differential Transformer V2](https://yomu.fyi/post/differential-transformer-v2.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Li Dong
- Published: Jan 20, 2026
- Type: Explainer

Differential Transformer V2 introduces an architectural refinement to differential attention that pairs and subtracts query heads within the same grouped-query attention group. The architecture doubles query heads while preserving key-value head counts, which avoids increasing key-value cache demands and removes the need for custom kernels. DIFF V2 replaces the per-head RMSNorm of its predecessor with a per-token, per-head projected lambda passed through a sigmoid function. This formulation expands the context root-mean-square range to between zero and root two, preventing massive gradient magnifications, mitigating attention sinks, and stabilizing large learning rate pretraining. Ablations confirm that sharing keys and values across subtracted heads and constraining lambda scaling are necessary to prevent training instability and elevated loss.


### [Introducing Waypoint-1: Real-time interactive video diffusion from Overworld](https://yomu.fyi/post/introducing-waypoint-1-real-time-interactive-video-diffusion-from-over.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Andrew Lapp, Louis Castricato, Scott Fox, Shahbuland Matiana, David Rossi
- Published: Jan 20, 2026
- Type: Announcement

Overworld introduced Waypoint-1, a real-time interactive video diffusion model controllable through text prompts, mouse movements, and keyboard inputs. The architecture uses a frame-causal rectified flow transformer trained on compressed latent representations across 10,000 hours of video game footage paired with control signals. Pre-training uses diffusion forcing with causal attention, while post-training incorporates self forcing via DMD to mitigate autoregressive rollout error accumulation, enable one-pass classifier-free guidance, and support few-step denoising. To achieve low latency on consumer hardware, Overworld developed WorldEngine, a pure Python streaming inference library. Utilizing AdaLN feature caching, static rolling key-value caching with Flex Attention, fused matrix multiplications, and PyTorch compilation, the 2.3B parameter Waypoint-1-Small model reaches up to 60 frames per second on an RTX 5090 GPU.


### [MongoDB.local San Francisco 2026: Ship Production AI, Faster](https://yomu.fyi/post/mongodb-local-san-francisco-2026-ship-production-ai-faster.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Author: Peter Richards
- Published: Jan 15, 2026
- Type: Announcement

MongoDB announced several features aimed at streamlining AI application development across its database and search ecosystem. The Voyage 4 embedding model family is now generally available, introducing cross-model compatibility within a shared embedding space alongside the video-capable voyage-multimodal-3.5 model. For in-database operations, MongoDB Community Edition introduced Automated Embedding in public preview to eliminate external synchronization pipelines for vector generation. Vector Search gained Lexical Prefilters, replacing the knnBeta operator with a dedicated vectorSearch operator and vector data type inside $search aggregations. Additionally, MongoDB made its intelligent assistant generally available in Compass and released the source code for its mongot search engine under the SSPL.


### [AI in Investment Management: 2026 Outlook (Part I)](https://yomu.fyi/post/ai-in-investment-management-2026-outlook-part-i.md)
- Company: [Two Sigma](https://yomu.fyi/company/two-sigma.md)
- Author: Emily Majewski
- Published: Jan 12, 2026
- Type: Explainer

Senior leaders and researchers at Two Sigma analyze the expanding role of artificial intelligence across quantitative investment management for 2026. Rapid model improvements are inverting traditional quantitative research workflows by vastly expanding hypothesis generation and shifting operational bottlenecks toward rapid evaluation. Rather than relying on large language models to execute trades independently, firms are integrating agentic workflows as an underlying operating system across data pipelines and portfolio rooms. However, autonomous agents present alignment challenges because they optimize proxy objective functions relentlessly without intrinsic contextual awareness. Sustained success relies heavily on human supervision, rigorous safety monitoring, and disciplined research execution rather than mere compute scale or model complexity.


### [Vision RAG: Enabling Search on Any Documents](https://yomu.fyi/post/vision-rag-enabling-search-on-any-documents.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Author: Thibaut Gourdel
- Published: Jan 12, 2026
- Type: Tutorial

Traditional retrieval-augmented generation relies on text parsing and optical character recognition to extract data from complex visual documents. These legacy techniques introduce high engineering effort, inconsistent accuracy, and heavy scaling costs. Vision RAG resolves these challenges by embedding entire visual documents directly through multimodal models like Voyage AI's voyage-multimodal-3.5, removing the need for text extraction pipelines. In this pipeline, dense vector embeddings map both text queries and visual assets into a unified representation space. At query time, the system retrieves relevant document images and supplies them alongside the text prompt to Anthropic's Claude 3.5 Sonnet to generate visually grounded responses.


### [Lyft’s Feature Store: Architecture, Optimization, and Evolution](https://yomu.fyi/post/lyft-s-feature-store-architecture-optimization-and-evolution.md)
- Company: [Lyft](https://yomu.fyi/company/lyft.md)
- Author: Rohan Varshney
- Published: Jan 6, 2026
- Type: Explainer

Lyft built a centralized Feature Store to optimize feature management, deployment, and data consistency for machine learning workflows across its rideshare platform. The system ingests batch features defined by Spark SQL and JSON metadata into Airflow DAGs, which simultaneously write data to Hive for model training and to an online serving layer for low-latency inference. Real-time streaming features are processed with Apache Flink and Apache Kafka before being ingested into the serving infrastructure. The online layer, dsfeatures, couples DynamoDB with a ValKey write-through LRU cache and OpenSearch to serve embeddings. Infrastructure optimizations reduced standard P95 read latency by a third while supporting over a trillion additional read and write operations.


### [NVIDIA Cosmos Reason 2 Brings Advanced Reasoning To Physical AI](https://yomu.fyi/post/nvidia-cosmos-reason-2-brings-advanced-reasoning-to-physical-ai.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Tsung-Yi Lin, Debraj Sinha
- Published: Jan 5, 2026
- Type: Announcement

NVIDIA released Cosmos Reason 2, an open reasoning vision-language model designed to improve multi-step planning, uncertainty handling, and physical AI decision-making. Available in 2B and 8B parameter configurations, the model increases input context support to 256K tokens, up from 16K in the prior version. The release adds spatial perception capabilities including optical character recognition, 2D and 3D point localization, bounding boxes, and trajectory generation for robotics. In domain adaptation tests on autonomous vehicle video captioning, fine-tuning the 8B model improved BLEU scores by 10.6% and LingoQA scores by 13.8%. The model leads the Physical AI Bench and Physical Reasoning leaderboards for open visual understanding models.


### [Introducing Falcon-H1-Arabic: Pushing the Boundaries of Arabic Language AI with Hybrid Architecture](https://yomu.fyi/post/introducing-falcon-h1-arabic-pushing-the-boundaries-of-arabic-language.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Basma Boussaha, Mohammed Alyafeai, Ahmed Alzubaidi, Leen AlQadi, Shaikha Alsuwaidi, Omar saif alkaabi, Hamza Alobeidli, Hakim Hacid
- Published: Jan 5, 2026
- Type: Announcement

The Technology Innovation Institute introduced Falcon-H1-Arabic, a family of Arabic language models released in 3B, 7B, and 34B parameter sizes. Built on a hybrid architecture, each block runs Mamba State Space Models and Transformer attention in parallel before fusing their representations at the output projection. Pre-training incorporated around 300 billion tokens across Arabic dialects, English, and multilingual content filtered via linguistic analysis. A post-training pipeline combining supervised fine-tuning and direct preference optimization addresses long-context utilization and alignment. Across evaluations on the Open Arabic LLM Leaderboard, the 34B model achieves roughly 75%, exceeding the performance of larger baseline systems like Llama-3.3-70B.


### [AprielGuard: A Guardrail for Safety and Adversarial Robustness in Modern LLM Systems](https://yomu.fyi/post/aprielguard-a-guardrail-for-safety-and-adversarial-robustness-in-moder.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Jaykumar Kasundra
- Published: Dec 23, 2025
- Type: Announcement

Modern language model systems face security and safety threats spanning prompt injection, jailbreaks, and tool manipulation across long-context agentic workflows. To address these vulnerabilities, researchers introduced AprielGuard, an 8B parameter causal decoder-only transformer based on a downscaled Apriel-1.5 Thinker Base model. The model detects 16 safety risk categories alongside binary adversarial attacks across standalone prompts, multi-turn dialogues, and agentic workflows. Trained on synthetic datasets created with Mixtral-8x7B, NVIDIA NeMo Curator, and SyGra, AprielGuard operates in both explainable reasoning and fast non-reasoning modes. Evaluation shows high precision and recall on safety and adversarial benchmarks, though trade-offs exist between explainability latency and non-reasoning performance.


### [Token-count-based Batching: Faster, Cheaper Embedding Inference for Queries](https://yomu.fyi/post/token-count-based-batching-faster-cheaper-embedding-inference-for-quer.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Author: Chengcheng Pei, Yuan Lin
- Published: Dec 18, 2025
- Type: Problem & solution

Serving embedding models for short search queries often suffers from poor GPU efficiency because traffic is spiky and memory-bound. Traditional time-window and request-count batching strategies lead to inconsistent GPU utilization, while tensor padding wastes compute on empty tokens. To resolve these bottlenecks, Voyage AI implemented token-count-based batching paired with padding removal in inference engines like vLLM. The architecture uses Redis with Lua scripts to atomically aggregate pending requests until reaching an optimal hardware saturation token threshold. Across production deployments, this approach achieved a 50% reduction in GPU inference latency with 3X fewer GPUs and improved throughput by up to 8×.


### [How Grab is accelerating growth with real-time personalization using Customer Data Platform scenarios](https://yomu.fyi/post/how-grab-is-accelerating-growth-with-real-time-personalization-using-c.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Saubhagya Awaneesh
- Published: Dec 18, 2025
- Type: Problem & solution

Grab’s Customer Data Platform (CDP) introduced Scenarios to address the limits of daily batch attributes for real-time personalization across marketing and user journeys. The capability combines Scribe event triggers, geo-fencing, optional historical CDP data, and predictive models, with a self-serve GUI for configuring and validating event sequences and APIs for programmatic management. Incoming events are processed through Flink, with historical data evaluated in StarRocks and outputs written to Kafka, Amphawa, or both for downstream consumption; configured Scenarios become available within an hour and are designed for latency under 15 seconds. More than 12 implementations are live, including a Grab Unlimited abandonment intervention that notified users 15 minutes after signup drop-off and produced over a 3% uplift in subscriber conversions versus non-real-time acquisition campaigns. Planned work includes scalable Scenario sharding, multiple output destinations, and delayed triggering.


### [Tokenization in Transformers v5: Simpler, Clearer, and More Modular](https://yomu.fyi/post/tokenization-in-transformers-v5-simpler-clearer-and-more-modular.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Ita Zaporozhets, Aritra Roy Gosthipaty, Arthur Zucker, Sergio Paniego, merve, Pedro Cuenca
- Published: Dec 18, 2025
- Type: Explainer

Transformers v5 overhauls its tokenization framework by separating tokenizer architecture from trained vocabularies. In contrast to v4's dual slow Python and fast Rust files, v5 consolidates each model tokenizer into a single file defaulting to the Rust-backed TokenizersBackend. The pipeline stages—normalizer, pre-tokenizer, model algorithm such as BPE or Unigram, post-processor, and decoder—are now directly exposed and configurable rather than buried in serialized files. Practitioners can instantiate blank tokenizer architectures and train custom vocabularies directly from iterators using native methods like train\_new\_from\_iterator while retaining model-specific formatting rules. The wrapper layer continues to bridge raw tokenization and model requirements by managing chat templates, context limits, and special token insertion.


### [The Open Evaluation Standard: Benchmarking NVIDIA Nemotron 3 Nano with NeMo Evaluator](https://yomu.fyi/post/the-open-evaluation-standard-benchmarking-nvidia-nemotron-3-nano-with.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Seph Mard, Isabel Hulseman, Besmira Nushi, Piotr Januszewski, Grzegorz Chlebus, VivienneZhang, Wojciech Prazuch, Pablo Ribalta, Nik Spirin, Ferenc Galko
- Published: Dec 17, 2025
- Type: Tutorial

Assessing whether large language model improvements stem from genuine advances or underspecified evaluation conditions remains a major challenge across the industry. Most published model evaluations omit critical execution parameters, prompt templates, harness versions, and runtime configurations. In response, NVIDIA released Nemotron 3 Nano 30B A3B alongside its complete, reproducible evaluation recipe built with the open-source NeMo Evaluator library. The library serves as an orchestration layer that unifies diverse benchmark harnesses under standard configurations while decoupling evaluation logic from underlying inference backends. Developers can execute the identical evaluation pipeline against local deployments or hosted endpoints using published YAML configurations and structured logging.


### [Codex is Open Sourcing AI models](https://yomu.fyi/post/codex-is-open-sourcing-ai-models.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: ben burtenshaw, shaun smith
- Published: Dec 11, 2025
- Type: Tutorial

Hugging Face Skills equips AI coding agents like OpenAI Codex to execute end-to-end machine learning workflows. By reading AGENTS.md files and interfacing via the Model Context Protocol, Codex automates dataset validation, training script updates, and job submissions to Hugging Face Jobs. The workflow supports methods including supervised fine-tuning, direct preference optimization, and reinforcement learning for models ranging from 0.5B to 7B parameters. Throughout execution, Codex tracks live metrics via Trackio, records benchmark evaluations against baselines, and maintains Markdown reports. Once training concludes, Codex merges LoRA adapters, applies GGUF quantization, and publishes the resulting models to the Hugging Face Hub for local deployment.


### [Tangle: An open-source ML experimentation platform built for scale (2025) - Shopify](https://yomu.fyi/post/tangle-an-open-source-ml-experimentation-platform-built-for-scale-2025.md)
- Company: [Shopify](https://yomu.fyi/company/shopify.md)
- Author: 2023-10-18
- Published: Dec 5, 2025
- Type: Announcement

Shopify introduces Tangle, an open-source, platform-agnostic machine-learning experimentation platform built for Search and Discovery workflows at commerce scale. It targets six stated failure modes, including repeated data preparation, poor reproducibility, slow deployment, unstructured notebooks, manual query tracking, and limited sharing. Users compose visual directed acyclic graph pipelines from reusable YAML components that wrap containerized or inline command-line programs in multiple languages, while file-based execution moves artifacts through storage. Tangle’s content-based caching reuses results when output content hashes remain identical, including artifacts from still-running executions, rather than relying only on lineage changes. Shopify reports a ten-hour pipeline completing in twenty minutes after one component changes, more than a year of compute time savings, and production use across ranking, semantic search, recommendation, and feature engineering workloads.


### [Introducing swift-huggingface: The Complete Swift Client for Hugging Face](https://yomu.fyi/post/introducing-swift-huggingface-the-complete-swift-client-for-hugging-fa.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Mattt
- Published: Dec 5, 2025
- Type: Announcement

Hugging Face released swift-huggingface, a dedicated Swift package offering complete Hub API integration, reliable file downloads, and inference provider access. The library addresses previous limitations in swift-transformers 1.0, where interrupted multi-gigabyte model downloads could not resume and cache structures differed from Python. To resolve cache duplication, swift-huggingface implements a Python-compatible content-addressed storage layout using symlinks and flock file locking. Authentication is standardized through a TokenProvider pattern supporting auto-detection, Keychain integration, static CI/CD tokens, and OAuth 2.0 sign-in with automatic token refresh. The package also provides URLSession-backed snapshot downloads with granular progress tracking and will soon replace the HubApi implementation inside swift-transformers.


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