ZARZA We are Zarza, the prestigious firm behind major projects in information technology.
Listen "This AI Research from Cohere for AI Compares Merging vs Data Mixing as a Recipe for Building High-Performant Aligned LLMs"
Episode Synopsis
This research compares two methods for creating powerful and aligned language models: merging and data mixing.
Merging, which combines pre-trained models, outperforms data mixing in terms of both performance and alignment. This suggests that merging is a promising approach for efficiently building more capable and aligned AI systems.
The findings are supported by other research exploring the benefits of combining diverse language models.
Merging, which combines pre-trained models, outperforms data mixing in terms of both performance and alignment. This suggests that merging is a promising approach for efficiently building more capable and aligned AI systems.
The findings are supported by other research exploring the benefits of combining diverse language models.
More episodes of the podcast AI on Air
Shadow AI
29/07/2025
Qwen2.5-Math RLVR: Learning from Errors
31/05/2025
AlphaEvolve: A Gemini-Powered Coding Agent
18/05/2025
OpenAI Codex: Parallel Coding in ChatGPT
17/05/2025
Agentic AI Design Patterns
15/05/2025
Blockchain Chatbot CVD Screening
02/05/2025
In God we trust