pyrodash-8b1024da·1 events·first seen Aliases: PyroDash
PyroDash is a framework that trains a small language model to emit control tokens during generation, triggering selective handoffs to a large LLM only when needed. The SLM policy is learned via a three-stage process culminating in Group Relative Policy Optimization with a reward balancing accuracy against normalized inference cost. On five mathematical reasoning benchmarks, the system achieves accuracy above LLM-only baselines while reducing cost by 20%+ at moderate settings, or cuts LLM calls to 0.012 per example at aggressive cost targets. The approach requires no separate router, LLM retraining, or access to LLM logits.