Recent advances in large language models (LLMs) have introduced explicit reasoning capabilities, yet the factors that truly drive their improved performance remain unclear. In this work, we disentangle the effects of reasoning quality and sequence length by fine-tuning 8B models on several Polish variants of the Mixture-of-Thoughts (MoT-PL) dataset, each representing a distinct reasoning style: Detailed, Summarized, BabyThink, Lengthy. We found that the model trained on high-quality reasoning traces achieved better average performance than all other models; neither longer reasoning with similar quality nor low-quality reasoning with similar length achieved similar gains. Qualitative and quantitative analyses further reveal that reasoning clarity, rather than verbosity, is the dominant factor driving model performance. These findings underscore the importance of reasoning content quality in LLM training and provide new insights into designing more effective reasoning-oriented datasets and models.

Many top-performing reasoning language models (RLMs) exhibit English-centric reasoning behavior, generating English chains of thought even when the prompt and final answer are in another language. Beyond its impact on multilingual reasoning performance, this also blocks downstream uses of the reasoning trace, such as debugging model errors and auditing decisions. We studied different approaches to RLM language adaptation by adapting Qwen3-4B-Thinking-2507 into Polish. We find that the accuracy collapse of SFT decomposes additively into termination and quality failure modes. On the other hand, KL-anchored fine-tuning mitigates these failures but exhibits a sharp plateau-then-cliff structure under reverse divergence: language match collapses from 0.89 to 0.37 as 𝛼 doubles, with no comparable structure under forward divergence. However, a second training stage leveraging GRPO helps close the remaining gap and recover the language match even from configurations past the cliff, revealing that the two stages divide labor: the supervised stage controls reasoning quality while the RL stage closes residual language match gaps. These findings suggest that, in a two-stage RLM post-training, the supervised phase can be considerably less constrained than current practice assumes.