Papers with pass@1
Can Multi-turn Self-refined Single Agent LMs with Retrieval Solve Hard Coding Problems? (2025.acl-srw)
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| Challenge: | Among the hardest tasks for humans are those found in competitive programming . language models (LMs) have not received enough attention as a domain to assess . |
| Approach: | They develop and evaluate a variety of LM inference techniques for competitive programming with these resources. |
| Outcome: | The proposed LM inference technique can solve 17 out of 18 problems that were previously unsolvable by any model or technique with just a few specific instructions. |
Self-Edit: Fault-Aware Code Editor for Code Generation (2023.acl-long)
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| Challenge: | Existing Large language models (LLMs) have low pass rates and accuracy on competitive programming tasks. |
| Approach: | They propose a generate-and-edit approach that uses execution results of generated code from LLMs to improve code quality on competitive programming tasks. |
| Outcome: | The proposed method improves pass@1 by 89% on APPS-dev, 31% on apps-test, and 48% on HumanEval over nine popular code generation LLMs with parameter sizes ranging from 110M to 175B. |
CodeT5+: Open Code Large Language Models for Code Understanding and Generation (2023.emnlp-main)
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| Challenge: | Existing code LLMs adopt a specific architecture or rely on a unified encoder-decoder network for downstream tasks, lacking flexibility to operate in the optimal architecture for a particular task. |
| Approach: | They propose to initialize code LLMs with frozen off-the-shelf LLM and explore instruction-tuning to align with natural language instructions. |
| Outcome: | The proposed model outperforms open-source LLMs on 20 code-related benchmarks. |
PairCoder: Pair Programming-Inspired Two-Agent Collaboration for Code Generation (2026.findings-acl)
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| Challenge: | Existing multi agent frameworks for large language models are brittle on code generation tasks. |
| Approach: | They propose a framework that brings pair programming to autonomous LLM collaboration. |
| Outcome: | Using PairCoder, large language models achieve better results on code generation tasks and reduce token usage by 40% to 70% on eight representative backbones. |
CODERL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment (2026.acl-long)
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Xue Jiang, Yihong Dong, Mengyang Liu, Deng Hongyi, Tian Wang, Yongding Tao, Zhi Jin, Wenpin Jiao, Ge Li
| Challenge: | Large Language Models excel at code generation by learning from vast code corpora, but a fundamental semantic gap remains between training on textual patterns and the goal of functional correctness . reinforcement learning with verifiable rewards (RLVR) approaches are inefficient for establishing a well-aligned connection between the textual representation of code and its execution semantics. |
| Approach: | They propose a novel approach that integrates execution semantics alignment into the RLVR training pipeline for code generation. |
| Outcome: | The proposed model outperforms baseline training and RLVR and shows strong applicability across RL and LLMs. |
Automating Android Build Repair: Bridging the Reasoning-Execution Gap in LLM Agents with Domain-Specific Tools (2026.eacl-long)
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| Challenge: | Large Language Models (LLMs) have been shown to be useful for building applications, but their use for fixing Android build errors remains underexplored. |
| Approach: | They propose a large-level language model agent with domain-specific tools for inspecting and manipulating the Gradle build environment. |
| Outcome: | The proposed agent outperforms a state-of-the-art coding agent that relies on a general-purpose shell significantly on 184 build errors. |
Functional Overlap Reranking for Neural Code Generation (2024.findings-acl)
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| Challenge: | Code Large Language Models (CodeLLMs) have ushered in a new era in code generation, but selecting the best code solutions remains a challenge. |
| Approach: | They propose a new reranking strategy that quantifies the functional overlap between solution clusters to provide a better ranking strategy for code solutions. |
| Outcome: | Empirical results show that the proposed method surpasses state-of-the-art methods on the pass@1 score. |
MapCoder: Multi-Agent Code Generation for Competitive Problem Solving (2024.acl-long)
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| Challenge: | Large language models (LLMs) have impressive proficiency in natural language processing, but performance in code generation tasks remains limited. |
| Approach: | They propose a framework that emulates the full cycle of program synthesis as observed in humans. |
| Outcome: | The proposed framework replicates the full cycle of program synthesis as observed in human developers. |
When to Continue Thinking: Adaptive Thinking Mode Switching for Efficient Reasoning (2025.findings-emnlp)
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Xiaoyun Zhang, Jingqing Ruan, Xing Ma, Yawen Zhu, Haodong Zhao, Hao Li, Jiansong Chen, Ke Zeng, Xunliang Cai
| Challenge: | Large reasoning models (LRMs) incur excessive computational overhead due to redundant reasoning, especially on simple tasks. |
| Approach: | They propose an Adaptive Self-Recovery Reasoning framework that suppresses unnecessary reasoning and enables implicit recovery. |
| Outcome: | The proposed framework suppresses unnecessary reasoning and enables implicit recovery. |
SolidCoder: Bridging the Mental-Reality Gap in LLM Code Generation through Concrete Execution (2026.findings-acl)
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| Challenge: | State-of-the-art code generation frameworks rely on mental simulations to validate buggy code. |
| Approach: | They propose a mental-reality gap between mental simulation and actual execution . they propose sandboxed execution with a simple principle: don't imagine—execute . |
| Outcome: | The proposed framework achieves state-of-the-art pass@1 performance on humanEval, CodeContests and APPS. |
Personalized Distillation: Empowering Open-Sourced LLMs with Adaptive Learning for Code Generation (2023.emnlp-main)
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| Challenge: | Recent studies have shown that close-sourced LLMs lack the ability to integrate into real-world applications due to their high associated costs and ethical concerns. |
| Approach: | They propose to use student model to refine its own solution by querying ChatGPT to generate task instruction and solution pairs and querying data to refine model. |
| Outcome: | The proposed model outperforms standard distillation with only one third of the data. |
Large Language Models for IT Automation Tasks: Are We There Yet? (2026.findings-acl)
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| Challenge: | Existing benchmarks rely on synthetic tasks that fail to capture the needs of practitioners who use IT automation tools. |
| Approach: | They evaluate 14 open-source and 3 proprietary LLMs and find that GPT-4.1-Mini achieves the best pass@10 rate of 23.9%, while Claude-3.5-Sonnet achieves best pass @1 performance. |
| Outcome: | The evaluated LLMs perform poorly in 126 tasks and show that they lack state reconciliation capabilities and lack module knowledge. |
ExecVerify: White-Box RL with Verifiable Stepwise Rewards for Code Execution Reasoning (2026.acl-long)
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| Challenge: | Existing methods for code execution reasoning are limited by the difficulty of the training data. |
| Approach: | They propose a model that uses reinforcement learning to reward correct answers from execution traces. |
| Outcome: | The proposed model improves pass@1 by up to 5.9% on code generation tasks over strong baselines. |
HumanEval Pro and MBPP Pro: Evaluating Large Language Models on Self-invoking Code Generation Task (2025.findings-acl)
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| Challenge: | Existing benchmarks for code generation tasks are inadequate, but performance declines on self-invoking tasks. |
| Approach: | They propose a general recipe for generating more challenging versions of existing benchmarks . they propose to use instruction-tuned models to evaluate LLMs on self-invoking code generation tasks . |
| Outcome: | The proposed model improves on humanEval and MBPP but on self-invoking code generation tasks. |
Humanity’s Last Code Exam: Can Advanced LLMs Conquer Human’s Hardest Code Competition? (2025.findings-emnlp)
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Xiangyang Li, Xiaopeng Li, Kuicai Dong, null Zhangquanhu, Rongju Ruan, Xinyi Dai, Yasheng Wang, Ruiming Tang
| Challenge: | o4-mini(high) and Gemini-2.5 Pro achieve pass@1 rates of only 15.9% and 11.4%, respectively. |
| Approach: | They propose a harmonized online–offline sandbox that guarantees fully reproducible evaluation. |
| Outcome: | The proposed test reflects the advanced reasoning and code generation ability of large language models. |
Can Language Models Replace Programmers for Coding? REPOCOD Says ‘Not Yet’ (2025.acl-long)
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| Challenge: | Existing benchmarks for code generation use short completions, synthetic examples, or focus on limited scale repositories, failing to represent real-world coding tasks. |
| Approach: | They propose a Python code-generation benchmark that contains 980 whole-function generation tasks with realistic dependencies from 11 popular projects. |
| Outcome: | The proposed benchmarks are short completions, synthetic examples, or focus on limited scale repositories, failing to represent real-world coding tasks. |
Learning to Insert [PAUSE] Tokens for Better Reasoning (2025.findings-acl)
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| Challenge: | Existing studies have explored incorporating special-purpose tokens into the training process to enhance reasoning capabilities. |
| Approach: | They propose a method for inserting dummy tokens consecutively just before reasoning steps to increase model effectiveness. |
| Outcome: | The proposed method outperforms fine-tuning and previous token insertion methods on multiple datasets and models. |
MavenCoder: Competitive Code Generation via Model Adaptive Planning Strategies and Multi-Perspective Verification Enhancement (2026.acl-long)
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| Challenge: | Recent advances in large language models (LLMs) have significantly enhanced automated program synthesis. |
| Approach: | They propose a model-adaptive and verification–enhanced framework for competition-level code generation that leverages adaptive assessment aligned with the model’s capabilities to select planning strategies while providing timely feedback and correction via multi-perspective verification. |
| Outcome: | The proposed framework outperforms existing state-of-the-art approaches on livecodebench, humanEval+, MBPP+, and codecontests, and achieves pass@1 results exceeding 3%–40%. |
IntentCoding: Amplifying User Intent in Code Generation (2026.findings-acl)
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| Challenge: | IntentCoding captures the influence of user intent by masking out the intent, and integrates seamlessly with existing decoding procedures. |
| Approach: | They propose a decoding strategy that captures the influence of user intent by masking out the intent and applies a multi-strength ensemble mechanism to amplify the effect of user intention during generation. |
| Outcome: | The proposed model significantly improves both constraint satisfaction and functional correctness compared to greedy decoding approaches. |
Rewarding the Rare: Uniqueness-Aware RL for Creative Problem Solving in LLMs (2026.findings-acl)
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Zhiyuan Hu, Yucheng Wang, Yufei He, Jiaying Wu, Yilun Zhao, See-Kiong Ng, Cynthia Breazeal, Anh Tuan Luu, Hae Won Park, Bryan Hooi
| Challenge: | Reinforcement learning (RL) is a paradigm for post-training large language models, but it suffers from exploration collapse . a new study finds that RL fails to reward correct solutions that exhibit rare high-level strategies . |
| Approach: | They propose a method that rewards correct solutions that exhibit rare high-level strategies by clustering rollouts according to their high- level solution strategies. |
| Outcome: | The proposed approach improves pass@k across large sampling budgets and increases area under the pass@K curve (AUC@K) without sacrificing pass@1. |
WebAnchor: Anchoring Agent Planning to Stabilize Long-Horizon Web Reasoning (2026.findings-acl)
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| Challenge: | Existing methods for reinforcement learning (RL)-based agents struggle with long-horizon planning and strategy coherence. |
| Approach: | They propose a reinforcement learning framework that decouples planning and execution. |
| Outcome: | The proposed framework outperforms baseline and first-step RL frameworks on four benchmarks. |