Stable personality is a key requirement for personal AI assistants, yet how reasoning models acquire such traits remains underexplored. Existing work largely treats personality as role-play and inherits the standard reasoning-model recipe: SFT warm-up followed by RLVR. We ask whether this recipe is appropriate for natural personality acquisition, where a trait should shape both final answers and intermediate reasoning, persist under prompt variation, and preserve general capability. Holding Stage 2 fixed as rubric-based GRPO, we vary Stage 1 on Qwen3-8B across four Big Five trait directions and compare standard SFT with MCQ-GRPO, a simple on-policy warm-up over the model’s trait-relevant choices and reasoning traces. We evaluate each endpoint using an LLM-judged Rubric for stylistic expression and TRAIT for introspective disposition. We find that SFT-then-GRPO achieves high stylistic scores but is weaker on the introspective probe. In contrast, MCQ-GRPO-then-GRPO yields stronger introspective acquisition at a smaller mathematical-reasoning cost. Weight- and activation-space analyses further support this finding, suggesting that personality acquisition is not only a question of how much trait is displayed, but where it is carried.

Debate education fosters critical thinking skills but often incurs high human costs. Recent advancements in Large Language Models (LLMs) show promise in automating counter-argument generation. However, it remains unclear how best to guide LLMs to target both implicit and critical premises. In this study, we systematically compare multi-step and one-step generation methods for counter-arguments across 100 debate topics. Our findings reveal that one-step approaches consistently outperform multi-step pipelines, owing to their better grasp of the “motion spirit,” minimized propagation of hallucinations, and avoidance of challenging intermediate tasks. Among premise-targeting methods, a one-step strategy that accounts for both implicit and explicit premises—Generated and Targeted Premise Attack (GTG)—emerges as the strongest performer in expert and automated evaluations. These results highlight the value of direct, integrated prompts for leveraging LLMs in complex argumentation tasks and offer insights for developing more effective automated debate agents.