What Matters for Latent Reasoning with Flow Matching
FLaRe flow-matching latent reasoning reaches 97% of explicit CoT accuracy at quarter latency.
The paper argues that latent thoughts should be useful, diverse, explainable, refinable with more compute, and cheaper than explicit chain-of-thought. Flow-based Latent Reasoning (FLaRe) specifies what the latent space encodes, where the flow is trained, how answers are read out, and a final stage trained on the model's own verified thoughts. Probes show improvement on all five requirements versus prior latent methods, and FLaRe reaches 97% of explicit CoT accuracy at about one quarter of the latency on arithmetic benchmarks.
- Defines five requirements for effective latent thoughts
- FLaRe trains flow matching in a learned latent space
- Final stage trains on the model's verified thoughts
- Reaches 97% of explicit CoT accuracy at quarter latency
From the original article
1 min read at huggingface.co
Latent reasoning lets a large language model (LLM) think in a continuous space and verbalize only the answer.
By Yassine Ouali, Adrian Bulat, Georgios Tzimiropoulos · huggingface.co
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