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Changing the planner's objective can boost goal-reaching success from 26% to 98% without any retraining or GPU resources.
Implicit computation in deep equilibrium models may be a mirage, as our findings reveal that the model often fails to improve upon its initialization, raising questions about their efficacy in complex reasoning tasks.
