arXiv (OAI Expanded)
open access
SMELT: Scaling Laws for Compute-Matched MoE Looped Transformers
Looped Transformers increase effective depth by iterating a shared block of layers, but most evaluations compare at fixed model size, conflating architectural advantage with extra FLOPs. We study looping on Mixture-of-Experts Transformers while closely matching per-token FLOPs, total non-embedding p
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
- Layer-wise MoE Routing Locality under Shared-Prefix Code Generation: Token-Identity Decomposition and Compile-Equivalent Fork Redundancy#120608
- VirnyFlow: Optimizing ML Pipelines for Accuracy, Fairness, and Stability at Scale#609789
- Benchmarking Deep Learning Predictions of Mutation-Induced Fold Switching#623286
- AutoRAGTuner: A Declarative Framework for Automatic Optimization of RAG Pipelines#155251
- RepairFormer: Automated Repair of Structured Inputs Using Transformers#429397
Record · ID 1069904
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