OpenCodeReasoning-II: A Simple Test Time Scaling Approach via
  Self-Critique
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.
687731d0257d4f04353707be
Wasi Uddin Ahmad
687731d0257d4f04353707bf
Somshubra Majumdar
claimed_verified
2025-07-16T08:15:41.697Z
6254f8e5d21e4cc386b881ad
https://cdn-avatars.huggingface.co/v1/production/uploads/1649899774659-6254f8e5d21e4cc386b881ad.jpeg
Somshubra Majumdar
user
smajumdar94
687731d0257d4f04353707c0
Aleksander Ficek
687731d0257d4f04353707c1
Sean Narenthiran
687731d0257d4f04353707c2
Mehrzad Samadi
687731d0257d4f04353707c3
Jocelyn Huang
687731d0257d4f04353707c4
Siddhartha Jain
687731d0257d4f04353707c5
Vahid Noroozi
687731d0257d4f04353707c6
Boris Ginsburg
OpenCodeReasoning-II: A Simple Test Time Scaling Approach via
  Self-Critique
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.
OpenCodeReasoning-II, a large dataset for code reasoning, enhances code generation and critique through a two-stage fine-tuning strategy, improving competitive coding performance and extending LiveCodeBench for C++.
Large Language Models test-time scaling distillation code generation critique OpenCodeReasoning-II supervised fine-tuning Qwen2.5-Instruct LiveCodeBench C++ programming language