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LLMs can be systematically debugged and improved by treating training data as code, allowing for targeted "patches" that fix concept-level gaps and reasoning errors.
SFT's instability and reward sparsity can be overcome with a novel Group Fine-Tuning (GFT) framework, leading to better LLM policies.
