This Ai Paper Investigates Test-time Scaling Of English-centric Rlms For Enhanced Multilingual Reasoning And Domain Generalization

Reasoning connection models, aliases RLMs, are progressively utilized to simulate step-by-step problem-solving by generating long, system reasoning chains. These models break down analyzable questions into simpler parts and build logical steps to scope answers. This chain-of-thought (CoT) attack has proven effective successful improving output quality, particularly successful mathematical and logical tasks. Despite multilingual capabilities successful galore modern ample models, nan attraction of investigation and training has remained mostly centered connected English, leaving a spread successful knowing really good these reasoning skills construe to different languages.

One awesome situation is that astir RLMs are fine-tuned connected English data, which limits their expertise to logic efficaciously successful different languages. This becomes particularly problematic for low-resource languages that person constricted training examples. The models whitethorn default to English reasoning patterns, producing lower-quality outputs erstwhile prompted successful different language. Furthermore, differences successful connection building tin origin reasoning errors, peculiarly erstwhile a exemplary trained successful 1 connection is expected to infer logic successful different without capable linguistic alignment.

Current techniques employment zero-shot aliases few-shot prompting strategies to negociate these limitations, often utilizing English arsenic a pivot language. Some efforts impact presenting prompts successful nan aforesaid connection arsenic nan query to sphere linguistic consistency. However, mini models person minimal benefits owed to constricted capacity, and moreover ample models show inconsistent capacity erstwhile reasoning successful low-resource languages. Despite multilingual pretraining, nan spread betwixt nan training and reasoning connection continues to inhibit meticulous multilingual reasoning.

The Brown University and MBZUAI investigation squad focused connected evaluating really expanding test-time computation, peculiarly done extended reasoning chains, tin impact nan multilingual reasoning abilities of English-centric RLMs. They investigated utilizing s1 models based connected nan Qwen2.5-Instruct architecture and fine-tuned connected 1,000 English STEM reasoning samples. These models were tested crossed various languages utilizing benchmarks for illustration MGSM and Global-MMLU to reply 4 halfway questions: nan effectiveness of crosslingual test-time scaling, language-mixing behaviors, capacity nether language-forcing, and cross-domain generalization.

In-depth experiments showed that models pinch much parameters importantly benefited from accrued test-time reasoning tokens. The 14B s1 model, erstwhile scaled to 8,000 reasoning tokens, achieved an mean accuracy of 81% crossed non-English languages successful MGSM. It outperformed models for illustration Qwen2.5-14B-Instruct by +23.1% successful French and +41.6% successful Swahili. Even though nan exemplary was trained only successful English, its capacity surpassed that of larger models specified arsenic DeepSeek’s R1-Distill-Qwen-32B successful respective high-resource languages. The study besides recovered that reasoning successful high-resource languages for illustration Chinese and English is much efficient, requiring less tokens and delivering amended results than successful low-resource languages for illustration Swahili aliases Telugu.

A cardinal study was nan “quote-and-think” behavior, wherever nan exemplary quoted non-English phrases from prompts and reasoned successful English. This accordant shape crossed languages for illustration Japanese and Russian suggested that nan exemplary utilized its multilingual knowing to construe non-English input without nonstop translation. Language-forcing experiments further confirmed that forcing reasoning successful high-resource languages yielded amended results, while strict reasoning successful low-resource languages led to important accuracy drops and computational inefficiencies.

Despite beardown results successful STEM-related tasks, capacity gains did not transportation to domains for illustration taste commonsense aliases humanities. In benchmarks for illustration FORK, expanding reasoning tokens sometimes reduced performance, indicating overthinking. The study concludes that while test-time scaling enhances multilingual reasoning successful high-resource languages, it does not generalize efficaciously to out-of-domain tasks aliases low-resource languages, indicating nan request for further investigation connected balanced multilingual training and domain adaptation.

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