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Solvable does not mean faithful: automated planners routinely validate and execute LLM-generated PDDL instances that completely corrupt the user's intended initial states and goal specifications.
Diminishing returns in parallel sampling can be overcome by generating diverse initial queries, leading to substantial performance gains in multi-hop question answering.
Recycling zero-variance queries during training can boost model performance, enabling a smaller 1.7B parameter model to match or exceed the accuracy of larger counterparts.
