Researchers have found that fine-tuning reasoning models on a small dataset of instruction-following examples can significantly improve their ability to control their Chain-of-Thought (CoT) reasoning traces. This improvement, observed across four different models, led to an increase in CoT controllability from an average of 2.9% to 8.8% on out-of-distribution tasks. The study suggests that even minimal fine-tuning can elicit latent controllability capabilities, indicating that poor CoT controllability in current models might not be a robust limitation. However, the researchers note that frontier AI labs may not prioritize such fine-tuning, and the implications for multi-turn or agentic settings remain unclear. AI
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RANK_REASON The cluster reports on an academic paper detailing fine-tuning experiments on reasoning models to improve CoT controllability.
