Reasoning Models Show Promise in Controlling Their 'Chain of Thought'
Analysis
This research explores a fascinating new dimension of how we can understand and control the behavior of Large Language Models (LLMs). The development of the CoT-Control evaluation suite is a major step forward, enabling us to test and improve the trustworthiness of reasoning models.
Key Takeaways
- •The CoT-Control evaluation suite is a new method for testing and improving the control of reasoning in LLMs.
- •Models currently struggle to control their 'Chain of Thought' to the same degree they can control their outputs.
- •The research suggests that current models are not easily tricked into misleading 'Chain of Thought' responses, which is promising for monitorability.
Reference / Citation
View Original"We show that reasoning models possess significantly lower CoT controllability than output controllability; for instance, Claude Sonnet 4.5 can control its CoT only 2.7% of the time but 61.9% when controlling its final output."
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