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CharacterFlywheel: Scaling Iterative Improvement of Engaging and Steerable LLMs in Production
arXiv – CS AI|Yixin Nie, Lin Guan, Zhongyao Ma, Anchit Gupta, Yipin Zhou, Xiao Li, Zhengping Zhou, Raymond Zeng, Gelin Zhou, Shigan Chu, Ajay Thampi, Wancen Mu, Nathan Shuster, Ketong Wang, Lin Chen, Jason Brewer, Derek Hao Hu, Alexander McCauley, Jason Weston, Sem Park, Na Zhang, Kevin Tang||3 views
🤖AI Summary
Meta presents CharacterFlywheel, an iterative process for improving large language models in production social chat applications across Instagram, WhatsApp, and Messenger. Starting from LLaMA 3.1, the system achieved significant improvements through 15 generations of refinement, with the best models showing up to 8.8% improvement in engagement breadth and 19.4% in engagement depth while substantially improving instruction following capabilities.
Key Takeaways
- →Meta's CharacterFlywheel process improved LLM performance across 15 generations using real-user traffic data from major social platforms.
- →Seven out of eight newly deployed models showed positive engagement lift, with top performers achieving up to 19.4% improvement in engagement depth.
- →Instruction following capabilities improved dramatically from 59.2% to 84.8%, while violations decreased from 26.6% to 5.8%.
- →The system integrates data curation, reward modeling, supervised fine-tuning, and reinforcement learning with continuous A/B testing.
- →The research advances scientific understanding of LLM deployment in production social applications serving millions of users.
#llm#meta#production-ai#social-media#reinforcement-learning#llama#engagement-metrics#fine-tuning#ab-testing#instagram
Read Original →via arXiv – CS AI
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