Training expert LLMs in domains with scarce fine-grained annotated data is admittedly challenging, often relying on multiple-choice questions (MCQs).However, standard outcomebased reinforcement learning (RL) on MCQs is risky.While outcome-based RL may improve accuracy, it frequently compromises the reasoning process, yielding internally inconsistent rationales that diverge from the final predictions.Existing solutions to supervise the reasoning process, such as large-scale Process Reward Models (PRMs), are prohibitively expensive.To address this, we propose CLARITY, a costeffective RL framework that enhances reasoning quality using a small, general-purpose LLM only.CLARITY integrates a consistency-aware reward mechanism with a 2-stage refine-thenmonitor training pipeline to enhance reasoning consistency, and a dynamic data reformulation strategy to better exploit annotated data available.Experiments demonstrate that CLARITY can improve the consistency of responses by 16.5% over standard outcome-based RL, and bring an improvement of 7.5% in final accuracy.Human evaluations further confirm substantial gains in factual correctness and reasoning coherence, leading to more trustworthy model outputs.Thus, CLARITY offers a generalizable solution that enables smaller models to effectively guide expert LLM training by monitoring reasoning consistency. 1

Adapting large language models (LLMs) to low-resource languages (LRLs) is constrained by the scarcity of task data and computational resources.Although Proxy Tuning offers a logit-level strategy for introducing scaling effects, it often fails in LRL settings because the large model's weak LRL competence might overwhelm the knowledge of specialized smaller models.We thus propose TRIMIX, a test-time logit fusion framework that dynamically balances capabilities from three different sources: LRL competence from a continually pretrained small model, task competence from high-resource language instruction tuning, and the scaling benefits of large models.It is data-and compute-efficient, requiring no LRL task annotations, and only continual pretraining on a small model.Experiments across four model families and eight LRLs show that TRIMIX consistently outperforms single-model baselines and Proxy Tuning.Our analysis reveals that prioritizing the small LRL-specialized model's logits is crucial for success, challenging the prevalent largemodel-dominant assumption.

While large language models (LLMs) have shown promise in translating extremely lowresource languages using resources like dictionaries, the effectiveness of grammar books remains debated.This paper investigates the role of grammar books in translating extremely low-resource languages by decomposing it into two key steps: grammar rule retrieval and application.To facilitate the study, we introduce ZHUANGRULES, a modularized dataset of grammar rules and their corresponding test sentences.Our analysis reveals that rule retrieval constitutes a primary bottleneck in grammarbased translation.Moreover, although LLMs can apply simple rules for translation when explicitly provided, they encounter difficulties in handling more complex rules.To address these challenges, we propose to represent grammar rules as code functions, motivated by their similarities in structures and the benefit of code in facilitating LLM reasoning.Our experiments show that using code rules significantly boosts both rule retrieval and application, ultimately resulting in a 13.1% BLEU improvement in translation.

Current large language models demonstrate deficiencies in understanding low-resource languages, particularly the minority languages in China.This limitation stems from the scarcity of available pre-training data.To address this accessibility challenge, we present MC 2 , a Multilingual Corpus of Minority Languages in China, which is the largest open-source corpus of its kind so far.MC 2 includes four underrepresented languages: Tibetan, Uyghur, Kazakh, and Mongolian.Notably, we focus on the less common writing systems of Kazakh and Mongolian, i.e., Kazakh Arabic script and traditional Mongolian script, respectively, which have been long neglected in previous corpus construction efforts.Recognizing the prevalence of language contamination within existing corpora, we adopt a quality-centric solution for collecting MC 2 , prioritizing accuracy while enhancing diversity.Furthermore, we underscore the importance of attending to the multiplicity of writing systems, which is closely related to the cultural awareness of the resulting models.The MC 2 corpus and related models are made public to the community 1 .