AutoTTS: LLM Agents Automatically Discover Test-Time Scaling Strategies for $40
AutoTTS proposes an environment-driven framework where LLM agents automatically discover test-time scaling strategies rather than researchers hand-crafting them. Formulating width-depth TTS as controller synthesis over pre-collected reasoning trajectories, the method discovers a Confidence Momentum Controller (CMC) that improves accuracy-cost tradeoff over manual baselines, generalizing across benchmarks and model scales — and costs only $39.90 and 160 minutes to run.
Why it matters
Automates the discovery of test-time scaling strategies, enabling self-improving inference pipelines at negligible cost and suggesting that TTS strategy design may be delegatable to agents.
Importance: 2/5
65 HF Daily upvotes; meta-level insight: TTS strategies can be auto-discovered by LLM agents for under $40, generalizing across benchmarks.