Papers by Renyu Fu
SeLaR: Selective Latent Reasoning in Large Language Models (2026.acl-long)
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| Challenge: | Recent latent reasoning approaches replace discrete tokens with soft embeddings or hidden states, but they often suffer from two issues: (1) global activation injects perturbations into high-confidence steps, impairing reasoning stability; and (2) soft embeds quickly collapse toward the highest-probability token, limiting exploration of alternative trajectories. |
| Approach: | They propose a lightweight and training-free framework that replaces discrete tokens with soft embeddings or hidden states to address these challenges. |
| Outcome: | Experiments on five reasoning benchmarks show that SeLaR outperforms standard CoT and state-of-the-art training-free methods. |