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This article discusses a method for debugging AI agents by identifying which specific layer in the agent's architecture produced an incorrect answer, rather than immediately tweaking the prompt.
This paper systematically studies layer-wise contribution in RL post-training for LLMs, finding that training a single middle transformer layer can recover or even surpass full-parameter RL gains, with consistent patterns across models and tasks.