2026-10-11 16:36 UTC

model-training

band: warmmomentum: stable score: 0.468
temperature history

Episodes (9)

Independent reproduction and evaluation will determine whether BananaMind 2 Pro was trained on a consumer GPU in roughly 20 days and achieved useful language-model quality at materially reduced training cost.
expiredknownscott: medium
The live open training run of a 535B-parameter, 23B-active mixture-of-experts model will publish usable checkpoints, training details, and results sufficient for outside scrutiny of the training process.
watchingconvergesscott: medium
The paper’s authors claim repeated exposure to limited domain data during LLM pretraining continues to improve specialized performance at scale without proportional growth in unique data, potentially lowering the data requirements for domain-model training.
expirednovelscott: medium
Mistral says it may use non-enterprise users’ inputs and outputs for model training by default unless they opt out, creating a material privacy distinction between standard and enterprise deployments.
resolvedconvergesscott: medium
Tahuna’s builders claim their newly open-sourced infrastructure combines compute provisioning, content-addressed synchronization, manifest-pinned training runs, and model serving, reducing the infrastructure small teams must build to run reproducible model experiments.
seednovelscott: low
Sakana AI claims its released PC-ALM method trains residual MLPs up to 1,000 layers using layer-local dynamics while nearly matching backpropagation on simple tasks, potentially overcoming predictive coding's deep-network credit-decay limitation.
seednovelscott: low
Baidu AI Cloud's Baige team claims its released LoongForge framework accelerates supported model-training workloads by up to 5.04 times over specified open-source baselines while aligning training loss curves, potentially reducing training costs and configuration work across multiple model families.
seednovelscott: low
Xiaomi's public MiMo 2.6 dashboard reportedly exposes live post-training progress, potentially giving outside developers visibility into an ongoing model-training run rather than only retrospective release results.
resolvednovelscott: low
Qlabs claims Dust — per-token activation-space perturbation with reward-weighted credit assignment — is the first zeroth-order method competitive with backprop at pretraining transformers (exceeding it at large populations and 10³–10⁴× more efficient than weight-space ES), and independent replication at its tested scales would establish search-based forward-only pretraining as a credible compute-rich alternative, while replication failure or scaling breakdown closes it.
seednovelscott: low

Trajectory notes