![[2509.26626] Recursive Self-Aggregation Unlocks Deep Thinking in Large Language Models](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2509.26626] Recursive Self-Aggregation Unlocks Deep Thinking in Large Language Models
Summary
The paper introduces Recursive Self-Aggregation (RSA), a novel method for enhancing large language models' performance through improved inference techniques, combining parallel and sequential scaling strategies.
Why It Matters
As large language models continue to evolve, RSA presents a significant advancement in optimizing their reasoning capabilities. By refining candidate solutions through aggregation, RSA not only improves performance across various tasks but also demonstrates the potential for more efficient model training and deployment, which is crucial for AI applications in diverse fields.
Key Takeaways
- RSA combines parallel and sequential scaling to enhance LLM performance.
- Empirical results show RSA achieves top-tier performance on multiple benchmarks.
- The method allows smaller models to compete with larger reasoning models.
- A novel reinforcement learning approach is proposed to improve solution aggregation.
- RSA's effectiveness is validated across diverse tasks and model families.
Computer Science > Machine Learning arXiv:2509.26626 (cs) [Submitted on 30 Sep 2025 (v1), last revised 24 Feb 2026 (this version, v2)] Title:Recursive Self-Aggregation Unlocks Deep Thinking in Large Language Models Authors:Siddarth Venkatraman, Vineet Jain, Sarthak Mittal, Vedant Shah, Johan Obando-Ceron, Yoshua Bengio, Brian R. Bartoldson, Bhavya Kailkhura, Guillaume Lajoie, Glen Berseth, Nikolay Malkin, Moksh Jain View a PDF of the paper titled Recursive Self-Aggregation Unlocks Deep Thinking in Large Language Models, by Siddarth Venkatraman and 11 other authors View PDF HTML (experimental) Abstract:Test-time scaling methods improve the capabilities of large language models (LLMs) by increasing the amount of compute used during inference to make a prediction. Inference-time compute can be scaled in parallel by choosing among multiple independent solutions or sequentially through self-refinement. We propose Recursive Self-Aggregation (RSA), a test-time scaling method inspired by evolutionary methods that combines the benefits of both parallel and sequential scaling. Each step of RSA refines a population of candidate reasoning chains through aggregation of subsets to yield a population of improved solutions, which are then used as the candidate pool for the next iteration. Empirically, RSA delivers substantial performance gains with increasing compute budgets across diverse tasks, model families and sizes. Notably, RSA with Gemini 3 Flash attains performance near the top o...