Scaling LLoCoT Benefits to Larger Models

Determine whether the benefits observed for LLoCoT with the 1.7-billion-parameter backbone extend to larger language models.

Background

The empirical study evaluates LLoCoT exclusively with a 1.7-billion-parameter Qwen3 backbone. Although the reported results show competitive code-generation accuracy and reduced reasoning latency, the paper does not establish whether these advantages persist as model size increases. Determining the scalability of the observed benefits to larger LLMs is therefore explicitly identified as unresolved.

References

First, all experiments use a 1.7B-parameter backbone, so it remains unclear whether the observed benefits extend to larger models.

— From Chain-of-Thought to Loops: Non-Autoregressive Latent Reasoning via Looped Transformers  (2610.11472 - García et al., 8 Oct 2026) in Section 5, Limitations