PanGu-Coder2: Boosting Large Language Models for Code with Ranking Feedback (2307.14936v1)

Abstract: LLMs for Code (Code LLM) are flourishing. New and powerful models are released on a weekly basis, demonstrating remarkable performance on the code generation task. Various approaches have been proposed to boost the code generation performance of pre-trained Code LLMs, such as supervised fine-tuning, instruction tuning, reinforcement learning, etc. In this paper, we propose a novel RRTF (Rank Responses to align Test&Teacher Feedback) framework, which can effectively and efficiently boost pre-trained LLMs for code generation. Under this framework, we present PanGu-Coder2, which achieves 62.20% pass@1 on the OpenAI HumanEval benchmark. Furthermore, through an extensive evaluation on CoderEval and LeetCode benchmarks, we show that PanGu-Coder2 consistently outperforms all previous Code LLMs.

An Analysis of PanGu-Coder2: Enhancing Code Generation through Ranking Feedback

The paper entitled "PanGu-Coder2: Boosting LLMs for Code with Ranking Feedback" presents significant advancements in the domain of LLMs for code generation. This manuscript leverages a novel optimization framework, RRTF (Rank Responses to align Test{content}Teacher Feedback), to enhance the performance of pre-trained Code LLMs. The authors introduce PanGu-Coder2, a model that showcases superior performance compared to existing models, achieving a noteworthy pass@1 score of 62.20% on the HumanEval benchmark. This success highlights the potential of incorporating ranking feedback mechanisms to improve code generation efficacy.

Core Contributions

The paper's primary contributions can be summarized as follows:

1. Introduction of the RRTF Framework: The authors propose a data-efficient and model-agnostic framework for reinforcing code generation in large pre-trained models. By ranking responses using test signals and teacher feedback, the framework effectively enhances the alignment of model outputs with expected solutions, circumventing traditional reward-based reinforcement learning approaches.
2. Development of PanGu-Coder2: The authors present PanGu-Coder2, a model demonstrating a nearly 30% improvement over its foundational model, achieving state-of-the-art performance across various benchmarks, namely HumanEval, CoderEval, and LeetCode.
3. Empirical Insights: The paper provides empirical evidence and insights leading to optimization of the training data and methodology, ensuring the generated solutions adhere closely to expected outputs. These efforts culminate in a model that not only exceeds previous benchmarks but also offers scalable enhancements for inference.

Technical Approach

The authors employ a novel training strategy involving the RRTF framework, which strategically ranks candidate solutions based on feedback that harnesses both automated test results and heuristic teacher-based preferences. This paradigm is inspired by concepts like RLHF but simplified for greater computational efficiency and ease of implementation. The RRTF framework includes:

• Sampling: Generation of diverse model outputs to ensure a broad learning base.
• Ranking: Ordering these outputs by feasibility and correctness using unit tests and teacher inputs.
• Training: Employing ranked feedback to inform a nuanced fine-tuning process that maximizes the relevance of learned solutions to the prompts given.

Evaluation and Results

The model's prowess is demonstrated across major benchmarks:

• HumanEval Benchmark: PanGu-Coder2 achieves a leading pass@1 rate at 61.64%, showcasing improvements over both open-source models and competitive benchmarks such as WizardCoder.
• CoderEval and LeetCode: The model maintains its lead, obtaining superior pass rates which manifest its ability to handle more complex, context-aware coding tasks.

The paper further presents results of improved memory efficiency and faster inference speeds when deploying model quantization techniques, especially with CTranslate2, without significant deterioration of solution quality.

Future Implications

The implications of this work are extensive. The RRTF framework offers a scalable, efficient alternative to traditional reinforcement learning paradigms, positioning itself as a feasible approach for enhancing large-scale LLMs in practical, resource-constrained environments. The reported capability of outperforming models of greater parameter counts with a leaner architectural footprint suggests potential pathways for optimizing code generation models further. As software development workflows continue incorporating AI tools, developments like PanGu-Coder2 will likely influence the integration of machine-learning-powered automation in coding environments.

The paper sets a firm foundation for future research to explore combinations of natural language processing techniques and model routing strategies for further elevations in LLM performance, primarily in code generation tasks.