Researchers automated LLM reasoning strategy design and cut token usage by 69.5%

Researchers from Meta, Google, and universities introduced AutoTTS, a framework that automatically discovers better test-time scaling strategies for large language models. In experiments, AutoTTS reduced token consumption by up to 69.5% without sacrificing accuracy, lowering inference costs for advanced reasoning workloads.

AutoTTS points to a shift from hand-designed prompting and inference heuristics toward automated optimization of model compute. If adopted in production, tools like this could make reasoning models more economically viable by dynamically balancing accuracy, latency, and token costs.