What Makes Effective Supervision in Latent Chain-of-Thought: An Information-Theoretic Analysis
arXiv:2606.20075v1 Announce Type: cross Abstract: Latent Chain-of-Thought (CoT) internalizes reasoning within continuous hidden states, offering a promising alternative to verbose discrete reasoning traces. However, robust latent reasoning remains difficult because outcome supervision provides weak learning signals and leaves latent trajectories prone to semantic drift. In this work, we analyze Latent CoT from an information-theoretic perspective and identify this failure as a dual collapse: gradient attenuation along the optimization path and representational drift in the latent space. We fur
The proliferation of advanced AI models highlights the need for more robust and efficient reasoning mechanisms, making research into Latent CoT increasingly urgent.
This research provides critical insights into addressing fundamental limitations in Latent Chain-of-Thought, which could significantly improve the reliability and efficiency of future AI systems.
Understanding and mitigating 'dual collapse' in Latent CoT could lead to more stable and interpretable AI reasoning, allowing for more powerful and trustworthy agentic systems.
- · AI researchers
- · Developers of AI agents
- · Enterprises deploying advanced AI
- · AI approaches heavily reliant on verbose reasoning traces
- · Systems with high susceptibility to semantic drift
Improved methods for training Latent Chain-of-Thought models become available.
More reliable and capable AI agents emerge as a result of enhanced reasoning stability.
The development of highly complex and autonomous AI systems accelerates, impacting white-collar workflows.
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