Think Less, Locally: Local Preference Learning for Reasoning Compression
LoCo is a local preference learning method for reasoning compression.
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- Primary sourceHugging Face
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LoCo is a local preference learning method for reasoning compression. It detects candidate overthinking states in a model's own reasoning traces, freezes the reasoning prefixes, regenerates shorter verified continuations from those states, and trains on the resulting state-conditioned preference pairs with DPO. Training data comes entirely from self-generated mathematical traces. On five math and three code benchmarks, the paper reports completion length reductions of 41.7%–45.4% on math and 23.4%–36.3% on code, with accuracy maintained or improved.
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Source claims not independently checked; results are reported on five math and three code benchmarks only.
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Source claims not independently checked; results are reported on five math and three code benchmarks only.
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LoCo detects candidate overthinking states from the model's own traces and regenerates shorter verified continuations from the same states.
Source: Hugging Face ↗Stored source-based brief · key point 1 · AINewsIQ paraphraseTraining uses state-conditioned preference pairs with DPO, aiming to teach when to continue reasoning and when to conclude.
Source: Hugging Face ↗Stored source-based brief · key point 2 · AINewsIQ paraphraseCompletion length drops 41.7%–45.4% on mathematics and 23.4%–36.3% on code benchmarks while accuracy is maintained or improved.
Source: Hugging Face ↗Stored source-based brief · key point 3 · AINewsIQ paraphraseTrained only on math traces, LoCo transfers to code generation benchmarks, achieving the best AUC across all code benchmarks.
Source: Hugging Face ↗Stored source-based brief · key point 4 · AINewsIQ paraphraseLimits from the stored brief
Source claims not independently checked; results are reported on five math and three code benchmarks only.
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Why this may matter
Long chain-of-thought drives inference cost in large reasoning models. A method that targets the local decision to stop reasoning, rather than shortening whole trajectories, could reduce cost while preserving accuracy, and the reported cross-domain transfer suggests the approach may generalize.
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Source claims not independently checked; results are reported on five math and three code benchmarks only.
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