LGSep 27, 2025

Emergence of Superposition: Unveiling the Training Dynamics of Chain of Continuous Thought

arXiv:2509.23365v215 citationsh-index: 13
Originality Incremental advance
AI Analysis

This provides theoretical insight into training dynamics for AI researchers, but it is incremental as it builds on prior work on CoT and superposition.

The paper tackles the problem of understanding how superposition mechanisms in chain of continuous thought (CoT) emerge during training, revealing that training dynamics in a two-layer transformer lead to a bounded index-matching logit that balances exploration and exploitation, resulting in superposition.

Previous work shows that the chain of continuous thought (continuous CoT) improves the reasoning capability of large language models (LLMs) by enabling implicit parallel thinking, and a subsequent work provided theoretical insight by showing that a two-layer transformer equipped with continuous CoT can efficiently solve directed graph reachability by maintaining a superposition of multiple reasoning traces in the continuous thought. However, it remains unclear how the superposition mechanism is naturally learned from gradient-based training methods. To fill this gap, we theoretically analyze the training dynamics of a simplified two-layer transformer on the directed graph reachability problem to unveil how the superposition mechanism emerges during training in two training stages -- (i) a thought-generation stage that autoregressively expands the continuous thought, and (ii) a prediction stage that converts the thought into the final answer. Our analysis reveals that during training using continuous thought, the index-matching logit, an important quantity which reflects the strength of the model's local search ability, will first increase and then remain bounded under mild assumptions. The bounded index-matching logit effectively balances exploration and exploitation during the reasoning process: the model will exploit local problem structures to identify plausible search traces, and assign comparable weights to multiple such traces to explore when it is uncertain about which solution is correct, which results in superposition. Our experimental results tracking the growth of logits further validate our theory.

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