LANG: Reinforcement Learning for Multilingual Reasoning with Language-Adaptive Hint Guidance
Multilingual reasoning in LLMs faces a persistent tension between maintaining input-language fidelity and preserving reasoning quality, with systems typically drifting toward English when prioritizing logic. LANG introduces a reinforcement learning framework that decouples these constraints through language-conditioned hints paired with adaptive scaffolding withdrawal and language-specific learning horizons. The approach matters because it expands RL-driven reasoning gains beyond English-dominant settings, addressing a real gap in how modern LLMs generalize across linguistic contexts. For teams building multilingual systems, this signals that reasoning enhancement no longer requires accepting language drift as inevitable.
Modelwire context
ExplainerThe paper's core contribution is not just that multilingual reasoning can be improved, but that the improvement comes from treating language preservation and reasoning quality as separable optimization targets rather than competing objectives. Prior work assumed this was a zero-sum trade-off.
This connects to the Hyperfitting work from May 21st, which identified that output quality improvements operate through mechanisms fundamentally different from conventional parameter tuning. LANG applies similar thinking to multilingual settings: instead of tweaking temperature or decoding parameters uniformly, it uses language-specific learning horizons and adaptive hint withdrawal to reshape how the model allocates reasoning effort across languages. Both papers challenge the assumption that a single tuning knob controls behavior across all contexts.
If LANG's gains replicate on held-out non-English benchmarks (Arabic, Mandarin, Japanese) that were not part of the RL training loop, the approach is genuine. If performance collapses when tested on languages with minimal pretraining data, the framework is just redistributing existing capacity rather than creating new multilingual reasoning capability.
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MentionsLANG · LLMs · Reinforcement Learning
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