DeepSeek R1 Distill Qwen 32B

Diamond, Pass@1 For solutions tasks in GPQA we we use main approach, which I Diamond. Diamond - this solutions tasks with steps: 1. tasks: with analysis tasks, main and goal. On this I task on components, key determination, limitations and question. 2. strategies: various possible approaches to solving, considering tasks. I and methods, most for given problems. 3. solution: solutions, its on clearly specific stages. I sequence mathematical which necessary execute. 4. verification: solution with several view, cases and approaches for reliability. I task several in order to result. 5. Evaluation final answer: result on and correctness, its with I that answer matches tasks. 6. Independent : step I still times all process solutions from to how if would I first this task, in order to identify errors or This structured approach allows me and thoroughly solve complex tasks, probability errors. In I that independent in end critically important for identification errors • Self-reported

Cons@64 AI: from context on basis prompts-chains Cons@64 should consider how for creation explanations. In difference from standard this technique thinking conclusions on more chains reasoning, solutions with high accuracy. Each step output should have 3 key : 1. context for step 2. step, on analysis 3. for to This technique supports reasoning on logical steps. Each output should directly on context, progress to solving. Cons@64 especially efficient for complex mathematical tasks, assignments by programming, logical evidence and other complex tasks, requiring step-by-step reasoning • Self-reported

Pass@1 In context testing Large Language Models (LLM) metric Pass@1 measures proportion tasks, which model can solve with first attempts. This metric reflects ability model generate correct answer "with ", without necessity several attempts or iterations. Pass@1 especially important for evaluation performance models in real scenarios use, where usually exact answer with first times. High score Pass@1 indicates on reliability and accuracy model in solving tasks without necessity additional or When Pass@1 test cases are evaluated how "", if first model answer matches criteria success (for example, correctly solves task, gives answer on question or successfully performs function). This metric often is used in analysis LLM for tasks, requiring accuracy, such how mathematical computation, programming or logical reasoning • Self-reported

Pass@1 In tasks mathematical reasoning often useful measure, how many times model can solve problem with first attempts. This score Pass@1. Standard method evaluation Pass@1 set for each problems (for example, 100 or 1000) and these which successfully solve task. However this method requires computational resources and can be More method — use evaluation Pass@1 with number for example, with for each tasks. In this case: Pass@1 = Pass@k / k where Pass@k — this proportion solutions at k attempts. This method gives evaluation, but with For more evaluation with number can use method "self-consistency", when model generates several answers for one tasks, and then most often occurring answer. This approach can accuracy, especially when model in its correct answers and in • Self-reported