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Overclocking LLM Reasoning: Monitoring and Controlling LLM Thinking Path Lengths
Overclocking LLM Reasoning: Monitoring and Controlling Thinking Path Lengths in LLMs by Roy Eisenstadt, Itamar Zimerman, Lior Wolf
This work investigates how large reasoning models internally track their thinking progress and how such processes can be monitored and controlled. To test this, we collect hidden representations from the final layer of the model for each token in a thinking trajectory $T = w_1w_2...w_N$. Conclusion These findings suggest that models internally track thinking progress and that this representation can be extracted and modified, opening doors for dynamic reasoning control and real-time interpretability.
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