CounterRoute: Self-Routed Reasoning via Hierarchical Counterfactual Credit Assignment

Ruochen Jiao; Besnik Fetahu; Zhenyu Shi; Priyanka Nigam

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arXiv · 2609.29109

CounterRoute: Self-Routed Reasoning via Hierarchical Counterfactual Credit Assignment

Ruochen Jiao·Besnik Fetahu·Zhenyu Shi·Priyanka Nigam

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Abstract

Reasoning-capable language models often produce long chains of thought when direct answers suffice, wasting inference compute. Many dual-mode models leave this choice to users. Automating it is challenging because routing targets evolve with the policy, initial mode preferences destabilize exploration, and sequence-level objectives entangle routing with response learning. We introduce CounterRoute, an online reinforcement-learning framework that jointly learns routing and modeconditioned responses in one shared policy directly from a native dual-mode checkpoint, without method-specific SFT warm-up. Paired current-policy counterfactual rollouts assign cross-mode credit only to the routing token, while within-mode GRPO trains response tokens. A paired-to-self-routed curriculum stabilizes early training with forced rollouts from both modes, then increases self-routed updates to improve autonomous routing. Across nine benchmarks, CounterRoute better balances accuracy and efficiency than heuristic and learned adaptive-routing methods. Relative to always-thinking checkpoints, it improves macro-average accuracy while reducing mean generated tokens by 51% for Qwen3-8B and 41% for Qwen3-14B. On instruction-following and commonsense benchmarks where direct answering is strong, think rates fall as low as 1% while response quality improves. Despite training only on math and instruction following, its routing behavior and response quality generalize to held-out coding, science, knowledge, and commonsense benchmarks.

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BibTeX RIS

Ruochen Jiao, Besnik Fetahu, Zhenyu Shi, Priyanka Nigam. 2026-09-24. CounterRoute: Self-Routed Reasoning via Hierarchical Counterfactual Credit Assignment. https://arxiv.org/abs/2609.29109

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