Entity trail

SparseLoCo

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

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Corpus findings

2026-09-23 / reddit-researcherTemplar simulates pipeline-parallel training that routes around offline stages with little loss penaltyTemplar's Crucible platform combines data-parallel replicas, pipeline compression and SparseLoCo. When an inner stage goes offline, activations and gradients bypass it for several steps. In a 178M-model simulation with 8 replicas, 4 stages per replica, a 1% per-replica failure chance per global step and six-step outages, validation loss stayed close to the no-failure baseline. Fixed projections shared across layers made the bypass more robust. The authors say this simulates learning effects only, not real worker replacement or cost savings (r/MachineLearning).
2026-03-26 / hn-researcherGrove: Distributed ML Training Across MacBooks via AirDrop's AWDL Protocol — Zero Network ConfigurationGrove is a Python library enabling distributed ML model training across multiple MacBooks using AWDL (Apple Wireless Direct Link) — the same protocol powering AirDrop — requiring zero network configuration. Run 'grove start train.py -n 4' on one machine and 'grove join' on others. Implements DiLoCo (independent training for 500 steps before syncing pseudo-gradients) and SparseLoCo (1-3% gradient compression for ~32x payload reduction). Uses a compiled Swift sidecar via Unix domain sockets since AWDL is locked behind Apple's Network.framework. 32 points on HN. Creative approach to democratizing ML training using consumer Apple hardware.
2026-03-16 / rss-researcherCovenant-72B: Largest Permissionless Decentralized LLM Pre-Training Run Completes via Bittensor — 70+ Global Participants, 146x Communication ReductionBittensor Subnet 3 (Templar) completed a 72B parameter pre-training run across 70+ global nodes on March 10, using SparseLoCo optimizer to reduce cross-node communication overhead 146x via sparsification, 2-bit quantization, and error feedback. The model scores 67.1 MMLU vs. LLaMA-2-70B's 65.6, trained on ~1.1T tokens, with Apache 2.0 weights on Hugging Face. First evidence that frontier-adjacent training is achievable without centralized infrastructure; SparseLoCo technique is directly applicable to federated fine-tuning pipelines.

Graph relationships

USES
Crucible
Crucible combines data-parallel replicas, pipeline compression, and SparseLoCo
Source finding
USES
Crucible -> SparseLoCo
Crucible combines data-parallel replicas, pipeline compression, and SparseLoCo
Source finding

Source trail

Templar (via r/MachineLearning)Swarnim Jain / Hacker News Import AI #449

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