arXiv (All)
open access
SOLO: Pretraining Billion-Parameter Language Models with Shared-Output Local Learning
Large language models are trained with backpropagation, whose global gradient coordinates all layers but forces each to hold its activations and wait for the gradient to pass back through every deeper layer. Conventional local learning removes this update locking by training each module to predict t
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- SOLO: Pretraining Billion-Parameter Language Models with Shared-Output Local Learning#1108698
- Mechanism Shift During Post-training from Autoregressive to Masked Diffusion Language Models#798735
- Mechanism Shift During Post-training from Autoregressive to Masked Diffusion Language Models#797321
- BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models#16564
- Block Parallelism For Efficient Distributed Long-Context Diffusion Language Model Training#1013840
Record · ID 1130027
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive