✨The Art of Scaling Test-Time Compute for Large Language Models

📝 Summary:
This study systematically compares Test-Time Scaling TTS strategies for LLMs. It finds no single dominant strategy, identifies distinct model trace-quality patterns, and shows optimal performance scales with compute. A practical guide for selecting TTS strategies is provided.

🔹 Publication Date: Published on Dec 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2512.02008
• PDF: https://arxiv.org/pdf/2512.02008
• Github: https://github.com/Aradhye2002/art_of_tts

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#LLM #TestTimeScaling #AI #DeepLearning #NLP

🔥 LLMs Improving LLMs: Agentic Discovery for Test-Time Scaling

💡 The paper proposes a novel approach to improve the performance of large language models through test-time scaling, which involves allocating additional computation during inference. Existing test-time scaling strategies are typically hand-crafted, relying on manual design and tuning of reasoning patterns and heuristics. This approach leaves much of the computation-allocation space unexplored, resulting in potential inefficiencies.

To address this limitation, the authors introduce AutoTTS, an environment-driven framework that automates the discovery of test-time scaling strategies. Instead of designing individual strategies, researchers can create environments where optimal strategies can be discovered automatically. The key to AutoTTS lies in constructing a discovery environment that provides a tractable control space and frequent, low-cost feedback for strategy search.

The authors formulate test-time scaling as a controller synthesis problem over pre-collected reasoning trajectories and probe signals. In this framework, controllers decide when to branch, continue, probe, prune, or stop, and can be evaluated cheaply without requiring repeated calls to the language model. To make the search tractable, the authors introduce beta parameterization, which enables fine-grained execution trace feedback to improve discovery efficiency.

The proposed approach is evaluated on mathematical reasoning benchmarks, where the discovered strategies demonstrate improved accuracy-cost tradeoffs over strong manually designed baselines. The discovered strategies also generalize to held-out benchmarks and model scales, indicating their robustness and flexibility. Notably, the entire discovery process incurs a relatively low cost of 39.9 dollars and 160 minutes, making it a practical and efficient solution.

Overall, the paper contributes a novel framework for automating test-time scaling strategy discovery, which has the potential to improve the performance of large language models while reducing the need for manual design and tuning. The authors also make their data and code available, facilitating further research and development in this area.

📅 Published on May 8

🔗 Links:
• arXiv: https://arxiv.org/abs/2605.08083
• PDF: https://arxiv.org/pdf/2605.08083
• Project Page: https://zhengkid.github.io/AutoTTS-web/
• GitHub: https://github.com/zhengkid/AutoTTS ⭐ 43

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#LargeLanguageModels #TestTimeScaling #AgenticDiscovery #AutomatedReasoning #LanguageModelOptimization