Adversarial Speculative Reasoning Boosts Long-Horizon Agent Accuracy
Speculative decoding is usually tuned for speed, so the finding that acceptance choices can silently shift reasoning accuracy across long agent runs is a practical surprise. Developers building multi-step agents can adopt this training trick to improve reliability without touching the target model's weights or outputs. The parameter cost — under 0.3% — makes it feasible even for teams serving open-weight models.