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Adversarial Speculative Reasoning Improves Long Horizon Agents

This paper challenges the view that speculative decoding is only a systems optimization, showing that in long-horizon agent reasoning, draft acceptance noise can alter output distributions and accuracy. The authors train the draft model to be rejected at critical decision points, forcing verification restarts so that key reasoning tokens are sampled from the target model under less noisy conditions. On complex agentic coding benchmarks, this approach improves end-to-end success rates without changing the target model, using only 0.06% to 0.28% of hosted parameters.