Topology-Enhanced Alignment for Large Language Models: Trajectory Topology Loss and Topological Preference Optimization (2026.findings-acl)
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| Challenge: | Existing algorithms for supervised fine-tuning and reinforcement learning from human feedback (RLHF) do not constrain how hidden states move from a user prompt to an answer. |
| Approach: | They propose a topology-enhanced alignment framework that regularizes these trajectories using 0-dimensional persistent homology. |
| Outcome: | The proposed framework regularizes semantic trajectory in hidden space using 0-dimensional persistent homology. |
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