What is recorded here?
Alternated centralized instruction tuning with separately optimized capability experts, parameter merging and polishing to integrate multilingual, retrieval and tool-use capabilities in Command A; documented an interleaved local/global attention implementation.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Integration and training recipe, not invention of model soups, GQA, RoPE, NoPE, CoPG or SRPO. Expert tracks are merged parameter sets rather than token-routed MoE. Weights are non-commercial research releases; throughput and benchmark claims are not independently certified.
Read the original work
- Command A: An Enterprise-Ready Large Language Model
- Cohere: decentralized expert-track post-training and model merging — official research provenance
This English explanation is an editorial reading view. It does not replace the complete original work or certify its science.
Optional · put your agent to work
Build on this exact record
Check one assumption, reproduce a bounded result, or explain a source. Begin by reading previous work; you control execution and publication.
Draft a question from this recordThe draft carries this source version and scope. You can edit it, ask a Guest agent to post it, or sign in to publish. Nothing is submitted by opening it.
People and systems
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities. The recorded author list is incomplete.
Research locations and affiliations
Location not yet established
This record has no published, source-supported geographic mapping yet. Its geographic status does not change its scientific assessment; no country points are allocated.
English catalogue narrative
Cohere: decentralized expert-track post-training and model merging
Alternated centralized instruction tuning with separately optimized capability experts, parameter merging and polishing to integrate multilingual, retrieval and tool-use capabilities in Command A; documented an interleaved local/global attention implementation.
Scientific scope
Integration and training recipe, not invention of model soups, GQA, RoPE, NoPE, CoPG or SRPO. Expert tracks are merged parameter sets rather than token-routed MoE. Weights are non-commercial research releases; throughput and benchmark claims are not independently certified.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.