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Data and training Latest updates

The ins and outs of the training side: dataset construction, synthetic data, pretraining and post-training methods, compute, and training costs.

33 条精选Past 30 days: 31 updatesTotal: 509 updates

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Nov 2Mon 21–33 updates
  1. Hacker News AI82

    China's First Frontier AI Export Controls: Mofcom Announcement No. 48

    On October 16, 2026, MOFCOM and MOST issued Announcement No. 48, proposing to add 40 technologies to China's export catalogue—23 of them in AI, covering frontier model weights, agent architectures, and offensive cyber capability—while an interim pre-export registration requirement is already in effect. The catalogue additions are a draft open for comment until November 15, 2026, whereas registration applies now, including to exports already started but not yet complete, and is triggered by licensing for overseas use or changes of ownership or control. MOFCOM says API, app, web, SaaS, and lightweight on-device use do not count as exporting a large model, but local deployment of a restricted model outside China does, and open-sourcing a new restricted model requires reporting under automatic licensing.

    Why it matters: It maps a new Chinese outbound control regime onto concrete enterprise exposure points—self-hosted deployments, intra-group code sharing, and M&A—making it useful for compliance and security teams tracking frontier AI supply-chain risk.

Oct 31Sat
  1. Hacker News AI76

    Open Science: The open-source AI workbench for scientific research

    OpenScience is an open-source research agent, available as a desktop app, browser workspace or CLI, that plans tasks, gathers evidence, runs code and experiments, and returns checkable results with every step visible. It offers shell, Python and R kernels, file access with explicit grants, remote compute, hundreds of bundled scientific skills, and connectors to databases such as ChEMBL, UniProt, PubMed and arXiv, with support for your own API keys, local models or the managed Ace option. It is Apache-2.0 licensed and reports 53/70 (75.7) on Terminal-Bench Science and 82.2 on BiomniBench-DA.

    Why it matters: Useful for researchers weighing an open-source, traceable agent workbench, since it lists concrete capabilities, model-access options and published benchmark scores.

  2. Wiz Research78

    “Black October” for AI Developer Platforms

    At least nine AI developer platforms disclosed incidents during October, with Fireworks, OpenCode and Warp reporting indications of an “agent swarm” attack and OpenCode and CommandCode attributing theirs to attempted “distillation.” Wiz describes downstream abuse of enterprise credentials tied to one vendor breach, including two keys used for roughly 800k and 1.7 million Bedrock calls respectively, likely over $100k in usage at list price. Wiz also links the keys to resale operations at llm-routing.com and xingjiabiapi.org, and publishes IOCs including 23.106.46.239 and 38.246.114.118.

    Why it matters: Provides Wiz's technical account of LLMJacking follow-on activity and IOCs from the October platform breaches.

Oct 30Fri
  1. Hacker News AI78

    Google value plunges by $500B as AI fears rattle Wall Street

    Alphabet lost just over $500bn in market value on Thursday, with its stock hitting a six-month low, after its quarterly update showed AI summaries on Search were affecting ad revenue and profits fell 25% even as total revenue grew 25% to $127.6bn. Microsoft and Amazon also reported this week, with Amazon shares falling 6% after hours on negative free cash flow of $42bn, while Microsoft's shares fell less than 1%. Analysts quoted said the results highlight the challenge of funding massive AI investments while sustaining established businesses, and where AI value will ultimately be captured.

    Why it matters: It captures a major market reaction to AI spending across three tech giants and the analyst debate over whether AI investments are paying off.

Oct 29Thu
  1. The Verge · AI80

    Anthropic is asking Wall Street to believe in AGI

    In a Decoder interview, SemiAnalysis analyst Joey Brookhart discusses Anthropic's S-1 filing for a Nasdaq IPO, which could begin trading as soon as next month at a valuation between $1.5 trillion and $2 trillion. He describes a company that scaled quickly to $70 billion-plus in ARR with strong gross margins, while noting that reaching $1 trillion in revenue would require moving beyond coding into white-collar work and other verticals. The conversation also covers the $80 billion non-cash charge tied to rising valuation, roughly $800 billion in long-term compute commitments, and Anthropic's stated intent to keep pushing training toward AGI.

    Why it matters: It offers a detailed analyst read on the first public financial disclosure from a frontier AI lab and the assumptions behind its valuation.

  2. Hacker News AI78

    Installing claude-mem uploads your last 30 days of prompts to cmem.ai, no opt-in

    The claude-mem installer (v15.2.0+, latest v15.5.0) automatically opens a setup page that reads local chat histories from Claude Code, Codex, Gemini CLI, Antigravity CLI and OpenCode and uploads the full text of prompts typed in the last 30 days—with session IDs, project names, folder paths and timestamps—to cmem.ai as soon as the page loads, with no way to stop it. The only checkbox, ticked by default and disabled during upload, does not prevent the upload when unticked, and cmem.ai confirmed the prompts were sent to a DeepSeek model via OpenRouter routed to the cheapest provider without blocking providers that retain or train on prompts. This contradicts the privacy policy's claim that nothing leaves the device without cloud sync and that memory content is not used for training; the article recommends blocking cmem.ai before running the installer.

    Why it matters: It documents a concrete, reproducible privacy failure in a widely used AI tool installer, with source-code references and vendor confirmation.

Oct 28Wed
  1. Artificial Analysis Articles60

    Announcing Artificial Analysis Intelligence Index v5

    Artificial Analysis released Intelligence Index v5, its biggest upgrade since v4, adding FrontierCode 2.0 and Terminal-Bench-Science and upgrading HLE-Diamond, AA-Omniscience v1.1 and CritPt v2, with 60% of the Index now based on private test sets or held-out answers. Claude Opus 5.5 (max) leads at 58, ahead of GPT-6 Astra (max, 57) and GPT-6.1 Sol (max, 55), while the open weights gap widens from 3 points to 12. It also launched the Progress Index v1, which tracks model capability over time on one continuous scale.

    Why it matters: It details a major benchmark revision and new leaderboard results, useful for readers tracking how frontier and open-weight models are measured.

Oct 23Fri
  1. Hugging Face Blog62

    Turning Database Grading into RL Rewards: Post-Training Agents with ThinkingBox

    Microsoft and Hugging Face describe using ThinkingBox's executable terminal-database-state checks as binary rewards to fine-tune Qwen3.8-27B with GRPO for 50 steps on an offline-selected pool of 187 tasks, raising pass@1 from 51.70% to 60.78% and tasks passing all 20 attempts from 38 to 107 on the 507-task ThinkingBox-Bench, while pass@20 dipped slightly from 453 to 446. The post details offline policy-relative difficulty selection via Jeffreys posteriors, the reward loop, cost-per-dependable-task falling from $24.36 to $8.00, and releases the training stack in microsoft/thinkingbox-training.

    Why it matters: It offers a concrete, reproducible recipe for converting stateful agent grading into RL rewards, plus an honest account of the repeatability-versus-coverage tradeoff.

Oct 22Thu
  1. Hugging Face Blog75

    NVIDIA Kumo Relational Outperforms Supervised Machine Learning on Relational Data, No Training Required

    NVIDIA introduces Kumo Relational, a pre-trained relational foundation model that predicts directly on enterprise databases via in-context learning in a single forward pass, requiring no task-specific training, feature engineering, or table flattening. Across 41 tasks on 15 databases, it beats supervised relational deep learning models and expert-engineered gradient-boosted tree pipelines, and on the SAP SALT benchmark it surpasses AutoGluon ensembles by 8% in mean reciprocal rank (MRR). It scales to databases with 500B+ rows and is available through NVIDIA's open-source structured-data-models and kumo-engine libraries or as an NVIDIA NIM.

    Why it matters: Useful for ML teams weighing whether to replace production XGBoost pipelines with a pre-trained relational model, though the reported gains come from NVIDIA's own paper and benchmarks.

Oct 21Wed
  1. NVIDIA Blog73

    NVIDIA Open Models Advance Europe’s Sovereign Intelligence Across Industries

    NVIDIA is expanding its family of open models across language, robotics, biology and quantum computing, including the new Nemotron 3.5 Super VL and Nemotron 3.5 Bolt models, Nemotron Retriever models, the Kumo family for structured business data, Cosmos 3.5 world models, Alpamayo 2 Edge for autonomous vehicles, BioNeMo for healthcare and science, and Ising for quantum computing. The company says the open models give organizations a foundation they can customize with local data and deploy on infrastructure they control, with Nemotron built using data and expertise from the NVIDIA Nemotron Coalition including European partners Black Forest Labs, H-Company and Mistral. Availability details include Nemotron 3.5 Super VL on Hugging Face, ModelScope, OpenRouter and build.nvidia.com, with Nemotron 3.5 Bolt E4B 4GB expected Friday, Oct. 23.

    Why it matters: The piece is a broad product roundup that shows how NVIDIA is positioning open models as a sovereignty-friendly foundation for European enterprises and researchers.

Oct 17Sat
  1. Google Research71

    Grounding AI agents in biological reality: Building blocks for autonomous wet labs

    Google researchers describe three projects aimed at connecting AI agents to real wet-lab experimentation: the VERID framework, a semantic digital twin of a lab workcell that tracks 3D state, runs fast virtual dry runs, blocks unsafe commands and logs synchronized video and actions; closed-loop aptamer design over three design-test cycles that raised on-target hit rates to 33.5% for myoglobin and 42.1% for α-thrombin; and agent-guided epitope excision for de novo binder design against FAP, which accelerated each folding cycle more than eightfold and, in a human–agent comparison at equal compute, nearly doubled the in-silico hit rate of a scientist working alone.

    Why it matters: It offers a concrete, example-rich account of how agents are being tied to physical lab hardware and closed experimental loops, including failure modes and safety safeguards.

Oct 15Thu
  1. Mistral AI67

    Introducing Timestral

    Mistral releases Timestral, its first family of time-series foundation models in four sizes from 5M to 209M parameters, with the largest ranking first on GIFT-Eval, TIME and fev-bench among pre-trained zero-shot models. Timestral produces probabilistic multivariate forecasts, supports past-only and known-future covariates, and runs on CPU without a GPU. It combines synthetic-first pre-training, covariate-aware attention and decoupled horizon forecasting, and can be fine-tuned on proprietary data through Mistral Forge.

    Why it matters: Useful for teams weighing time-series foundation models, since it reports benchmark standings, a size-versus-accuracy trade-off and CPU latency figures alongside the adaptation options.

Oct 8Thu
  1. Microsoft Research71

    Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses

    Microsoft Research Asia introduces Harnessed Agentic RL, a paradigm in which the same agent harness used in deployment participates directly in reinforcement learning, and open-sources Agent Lightning v1.0, a roughly 3,500-line framework with an API Gateway, Rollout Controller, and Customized Trainer built on verl. Agents run as standard Kubernetes jobs without commercial sandbox services, and Collocated Async RL shares GPUs between rollout and updates, achieving about a 2x end-to-end speedup over synchronous RL. An end-to-end coding agent pipeline raised Qwen3.5-9B from 41.8% to 56.4% Pass@1 on SWE-bench Verified using about 6,000 training samples.

    Why it matters: It offers a concrete, reproducible recipe for training deployed agents in place, with code size, infrastructure, and benchmark numbers stated explicitly.