Source of reduced degradation on general reasoning performance

Determine whether the smaller degradation in MMLU-Pro performance produced by Self-Routing results from its conditional assignment of optimization paths according to the model’s own outputs, thereby reducing the influence of noisy or overly specialized training signals and limiting drift from general-purpose behavior.

Background

The paper reports that mathematical post-training generally reduces MMLU-Pro performance relative to the base model, but Self-Routing exhibits the smallest degradation among the post-training methods evaluated. The authors attribute this observation tentatively to the method’s conditional routing rather than uniform updates.

This explanation is explicitly presented as a conjecture rather than an established causal result. Confirming it would require isolating whether adaptive assignment among GRPO, OPSD, regularization, and skipping actually reduces noisy or overly specialized updates and thereby preserves broader reasoning capabilities.