Spotify accelerates conversational agent launch with synthetic data and self-improvement loops
Spotify detailed a new method for training conversational recommendation agents in a paper posted on arXiv. The system uses synthetic data generation to simulate multi-turn user conversations, enabling testing before real-world deployment. A self-improvement loop combines variance-based contrastive optimization with automated error correction by a coding agent, boosting performance by 8% over…
Key points
- Spotify’s pipeline generates synthetic multi-turn conversations for agent testing before launch
- Self-improvement loop uses contrastive optimization and coding agents to fix errors automatically
- Production tests yield 14% more listening time, 5% more weekly active users, and 5% fewer skips
The approach has been deployed in production, shortening development cycles for Spotify’s upcoming conversational agent. Online A/B tests show improvements of 14% in user listening time, a 5% rise in weekly active users, and a 5% drop in skip rates compared to a prior session-based system. The paper frames this as a scalable framework for industry-wide development of such agents.
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