Finetuning a Reasoning LLM with Supervised or Reinforcement Learning?
Critical insights on training data representation and loss management in LLM finetuning.
In the finetuning of LLMs, key considerations include how to represent training data, which tokens should incur loss, and when to transition from supervised fine-tuning to reinforcement learning. Properly structuring training data is essential for enhancing model performance. Additionally, incorporating various scenarios alongside tool-calling examples is crucial for developing the model's decision-making capabilities.