Latent Thought Flow: Efficient Latent Reasoning in Large Language Models
- Published
- Source
- arXiv
- Paper number
- 444
- Field
- AI / General
- arXiv ID
- 2606.16222
Key points
- This is a new approach that solves the language-space bottleneck of Chain-of-Thought, meaning the token-decoding overhead, by reasoning in latent space.
- A continuous GFlowNet learns a distribution over latent reasoning trajectories proportional to reward, preserving diverse accurate and efficient paths.
- Entropy-Weighted Subtrajectory Balance propagates terminal rewards to intermediate steps, which addresses the sparse-supervision problem.
- Reference-prior regularization provides stable early exploration, and annealing then shifts the model toward reward-based trajectories.
- Compared with CoLaR and ReGuLaR, it improves fine-tuning accuracy by 12.9 percent and shortens reasoning length by 34.5 percent.
- In transfer-learning settings, it also improves accuracy by 6.0 percent and shortens reasoning length by 19.9 percent, which demonstrates generality.
Paper links
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