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[2510.03777] GuidedSampling: Steering LLMs Towards Diverse Candidate Solutions at Inference-Time
Summary
The paper introduces GuidedSampling, a novel inference algorithm designed to enhance the diversity of candidate solutions generated by large language models (LLMs) during inference, outperforming traditional methods.
Why It Matters
GuidedSampling addresses a critical limitation in existing inference algorithms by improving the diversity of generated solutions. This advancement is significant for applications in AI where varied outputs are essential for creativity and problem-solving. Enhanced diversity can lead to better performance in complex tasks, making this research relevant for developers and researchers in AI and machine learning.
Key Takeaways
- GuidedSampling improves diversity in candidate solutions compared to traditional Repeated Sampling.
- The algorithm separates exploration and generation phases, enhancing solution variety.
- Empirical results show a ~21.6% performance improvement at pass@50 across benchmarks.
- Models trained with GuidedSampling exhibit a 9.7% increase in performance at pass@5.
- The average number of concepts utilized per instance increased from 1.67 to 3.03.
Computer Science > Artificial Intelligence arXiv:2510.03777 (cs) [Submitted on 4 Oct 2025 (v1), last revised 14 Feb 2026 (this version, v2)] Title:GuidedSampling: Steering LLMs Towards Diverse Candidate Solutions at Inference-Time Authors:Divij Handa, Mihir Parmar, Aswin RRV, Md Nayem Uddin, Hamid Palangi, Chitta Baral View a PDF of the paper titled GuidedSampling: Steering LLMs Towards Diverse Candidate Solutions at Inference-Time, by Divij Handa and 5 other authors View PDF HTML (experimental) Abstract:Repeated Sampling (RS) is a simple inference-time algorithm that has been shown to improve model performance on complex tasks. Although it is an effective way of scaling inference time, it often struggles to generate diverse solution candidates, frequently relying on the same underlying approach to solve the problem and thus producing redundant samples. To address this limitation, we propose a new inference algorithm, GuidedSampling, which decouples the exploration and generation phases during inference, increasing diversity of generated candidate solutions. The exploration phase identifies multiple concepts that can be utilized to solve the problem, while the generation phase applies a specific concept to provide final solution candidates. We first define the theoretical bounds of GuidedSampling and then empirically demonstrate that it improves the performance of base model at pass@50 by on an average ~21.6% across various benchmarks compared to RS. Furthermore, models tr...