Challenge: Existing methods to enhance code generation performance include integrating compiler feedback.
Approach: They propose a method that integrates compiler feedback to improve one-off code generation performance.
Outcome: The proposed method improves one-off code generation performance on three benchmarks and can be applied to other domains that focus on final results and require long reasoning paths.

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Challenge: erroneous code generation methods amalgamate feedback and correct code as target sentences . a new approach to code generation with feedback is needed to improve model performance .
Approach: They propose a learning-based code generation model with execution feedback that integrates feedback and correct code as target sentences.
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Turning the Tide: Repository-based Code Reflection (2025.findings-emnlp)

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Challenge: Code large language models (LLMs) enhance programming by understanding and generating code across languages.
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ReflectEvo: Improving Meta Introspection of Small LLMs by Learning Self-Reflection (2025.findings-acl)

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Challenge: ReflectEvo-460k is a large-scale, comprehensive, self-generated reflection dataset with broadened instructions and diverse multi-domain tasks.
Approach: They propose a pipeline that iteratively generates self-reflection for self-training and a large-scale reflection dataset with broadened instructions and diverse multi-domain tasks.
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Instruct-of-Reflection: Enhancing Large Language Models Iterative Reflection Capabilities via Dynamic-Meta Instruction (2025.naacl-long)

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Challenge: Existing approaches involve models iterating and improving their previous responses based on internal reflection ability or external feedback.
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StepCoder: Improving Code Generation with Reinforcement Learning from Compiler Feedback (2024.acl-long)

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Challenge: Existing work integrates reinforcement learning with compiler feedback to enhance code generation quality but the long code generated by LLMs makes RL exploration ineffective.
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Reflective Decoding: Beyond Unidirectional Generation with Off-the-Shelf Language Models (2021.acl-long)

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Challenge: Existing methods for generating text are unsupervised and require supervision.
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OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement (2024.findings-acl)

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Challenge: OpenCodeInterpreter-33B provides a high level of performance for code generation, executing, and iterative refinement.
Approach: They propose a family of open-source code systems for generating, executing, and iteratively refining code.
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Meta-Reflection: A Feedback-Free Reflection Learning Framework (2025.acl-long)

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Challenge: Existing approaches to improve large language models' ability to understand and reason are limited by external feedback.
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Can docstring reformulation with an LLM improve code generation? (2024.eacl-srw)

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Challenge: Existing approaches focus on training, fine-tuning or prompting LLMs to generate better outputs given the same input.
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CodeDPO: Aligning Code Models with Self Generated and Verified Source Code (2025.acl-long)

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Challenge: Existing training methods for code generation do not improve code correctness and efficiency.
Approach: They propose a framework that integrates preference learning into code generation to improve code correctness and efficiency.
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