SOLAR: Scalable Optimization of Large-scale Architecture for Reasoning
Journal:
arXiv
Published Date:
Mar 6, 2025
Abstract
Large Language Models excel in reasoning yet often rely on Chain-of-Thought
prompts, limiting performance on tasks demanding more nuanced topological
structures. We present SOLAR (Scalable Optimization of Large-scale Architecture
for Reasoning), a framework that dynamically optimizes Chain-of-Thought (CoT),
Tree-of-Thought (ToT), and Graph-of-Thought (GoT) topologies to boost accuracy
and efficiency. Our Topological-Annotation-Generation (TAG) system automates
dataset creation, annotation, and difficulty segmentation, leading to stronger
post training and test-time performance. We also propose Topological-Scaling, a
curriculum-learning-based approach that adaptively combines post training and
inference scaling to each task. On MATH and GSM8K, SOLAR delivers notable
gains: +5% accuracy with Topological Tuning, +9% with Topological Rewarding,
and +10.02% with Hybrid Scaling, while reducing response length by over 5%,
lowering inference latency. To further enhance efficiency, we introduce a
multi-task Topological Reward Model (M-TRM) that selects both the optimal
reasoning topology and final answer in a single pass, eliminating multiple
single-task TRMs. Remarkably, M-TRM also surpasses all single-task TRMs,
improving accuracy by +10% and rank correlation by +9%. Overall, SOLAR
establishes a new benchmark for scalable, high-precision LLM reasoning and
introduces a fully automated, dynamic topology competition mechanism.
