aiqbench analysis
The paper proposes training only the first "root" token of a reasoning model's output, the choice of whether to open a thinking block, using group-relative rewards. The authors claim this lets models adapt how much they reason to query difficulty at about 2% of the training compute of prior adaptive-reasoning methods. The claims are reported from an arXiv preprint, so readers should check the benchmarks, model scales, and baselines before treating the efficiency figure as settled.
Efficiently Learning To Reason or Not to Reason: Root-token Policy Optimization for Adaptive Thinking
arXiv:2610.17767v1 Announce Type: cross Abstract: Large reasoning models (LRMs) achieve strong performance by externalizing explicit reasoning traces before producing the answer, yet suffer from overthinking challenge that allocates uniformly heavy computation to queries of varying difficulty. While proprietary models mitigate this via opaque routing, open-source LRMs still lack an efficient mechanism to internalize adaptive reasoning due to both expensive training cost and limited disclosure of training recipes. In response, we introduce RPO (Root-token Policy Optimization), a framework that enables LRMs to self-determine when to reason by training only the initial root token (e.g., whether to invoke the tag) via group relative reward and group-wise advantages. By focusing on this pivotal branching point, RPO drastically reduces training overhead and VRAM usage. Across multiple model families and scales, RPO learns difficulty-aware adaptive thinking at just ~2% of the training compute of prior adaptive-reasoning methods.
Abstract:Large reasoning models (LRMs) achieve strong performance by externalizing explicit reasoning traces before producing the answer, yet suffer from overthinking challenge that allocates uniformly heavy computation to queries of varying difficulty. While proprietary models mitigate this via opaque routing, open-source LRMs still lack an efficient mechanism to internalize adaptive reasoning due to both expensive training cost and limited disclosure of training recipes. In response, we introduce RPO (Root-token Policy Optimization), a framework that enables LRMs to self-determine when to reason by training only the initial root token (e.g., whether to invoke the <think> tag) via group relative reward and group-wise advantages. By focusing on this pivotal branching point, RPO drastically reduces training overhead and VRAM usage. Across multiple model families and scales, RPO learns difficulty-aware adaptive thinking at just ~2% of the training compute of prior adaptive-reasoning methods.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2610.17767 [cs.CL]
(or arXiv:2610.17767v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.17767 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 236 words
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