SeLaR: Selective Latent Reasoning in Large Language Models
Chain-of-Thought (CoT) has become a cornerstone of reasoning in large language models, yet its effectiveness is constrained by the limited expressiveness of discrete token sampling. Recent latent reasoning approaches attempt to alleviate this limitation by replacing discrete tokens with soft embeddings (probability-weighted mixtures of token embeddings) or hidden states, but they commonly suffer from two issues: (1) global activation injects perturbations into high-confidence steps, impairing reasoning stability; and (2) soft embeddings quickly collapse toward the highest-probability token, limiting exploration of alternative trajectories. To address these challenges, we propose SeLaR (Se...
| Authors | Renyu Fu, Guibo Luo |
|---|---|
| Primary category | cs.CL |
| Published | 9 April 2026 |
| Last updated at source | 19 April 2026 |
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