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Optimizing Fine-Tuning for Reasoning LLMs

The author faces a challenge in determining the best approach for fine-tuning small LLMs on annotated conversational data. They need to decide on the optimal training method, including whether to use supervised learning, reinforcement learning, or a combination of both, to improve the model's reasoning and tool-calling behavior.

Computing & Technology, Computer Science, Machine Learning