This research maps Long CoT trajectories in LLMs as topological structures driven by deep-reasoning, self-reflection, and self-exploration interactions.
The Mole-Syn distribution-transfer-graph method synthesizes effective semantic isomers to facilitate fast entropy convergence and stabilize reinforcement learning.
This structural approach minimizes trajectory competition during fine-tuning and improves performance across reasoning benchmarks.
Sources: Paper
