Selective TTS for Complex Tasks with Unverifiable Rewards
Published:Dec 27, 2025 17:01
•1 min read
•ArXiv
Analysis
This paper addresses the challenge of scaling LLM agents for complex tasks where final outcomes are difficult to verify and reward models are unreliable. It introduces Selective TTS, a process-based refinement framework that distributes compute across stages of a multi-agent pipeline and prunes low-quality branches early. This approach aims to mitigate judge drift and stabilize refinement, leading to improved performance in generating visually insightful charts and reports. The work is significant because it tackles a fundamental problem in applying LLMs to real-world tasks with open-ended goals and unverifiable rewards, such as scientific discovery and story generation.
Key Takeaways
- •Proposes Selective TTS, a process-based refinement framework for multi-stage pipelines.
- •Addresses the challenge of unverifiable rewards in complex tasks.
- •Demonstrates improved performance in generating visually insightful charts and reports.
- •Mitigates judge drift and stabilizes refinement by pruning low-quality branches.
Reference
“Selective TTS improves insight quality under a fixed compute budget, increasing mean scores from 61.64 to 65.86 while reducing variance.”