OpenCodeReasoning-II: A Simple Test Time Scaling Approach via
  Self-Critique
Authors: name: Wasi Uddin Ahmad, name: Somshubra Majumdar, name: Aleksander Ficek, name: Sean Narenthiran, name: Mehrzad Samadi, name: Jocelyn Huang, name: Siddhartha Jain, name: Vahid Noroozi, name: Boris Ginsburg

Recent advancements in reasoning-based Large Language Models (LLMs),
particularly their potential through test-time scaling, have created
significant opportunities for distillation in code generation and critique.
However, progress in both areas fundamentally depends on large-scale,
high-quality datasets. In this work, we introduce OpenCodeReasoning-II, a
dataset consists of 2.5M question-solution-critique triples (approx. 35K unique
programming questions), making it nearly twice the size of the previous largest
publicly available code reasoning dataset. In this work, we employ a two-stage
supervised fine-tuning strategy. The first stage focuses on fine-tuning for
code generation, while the second stage involves the joint training of models
for both code generation and critique. Our resulting finetuned Qwen2.5-Instruct
models achieve performance in code generation that either exceeds or equals the
best prior open-weight distilled models. Notably, the integration of our code
generation and critique models leads to significant improvements in competitive
coding performance. Furthermore, we present an extension of the LiveCodeBench
benchmark to specifically support the C++ programming language, thereby
facilitating more comprehensive LLM evaluation using this benchmark.