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A novel type of benchmark evaluating the inductive reasoning capabilities of LLMs that is inspired by the forward reconstruction task from historical linguistics but is formulated in an extremely simple, general way (in the form of Programming by Examples).
A separate critic model that is specialized in high-level planning to steer the coding agent in inference and reduce the total inference costs for some coding agents by solving tasks in fewer steps is trained.
This work systematically study repeated alignment and misalignment cycles using controlled fine-tuning loops while tracking behavioral performance, and LoRA representations throughout training, finding that both misalignment and realignment are highly sensitive to superficial dataset characteristics.
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