Austria

Most progress in recent coder models has been driven by supervised fine-tuning (SFT), while the potential of reinforcement learning (RL) remains largely unexplored, primarily due to the lack of reliable reward data/model in the code domain. In this paper, we address this challenge by leveraging automated large-scale test-case synthesis to enhance code model training. Specifically, we design a pipeline that generates extensive (question, test-cases) pairs from existing code data. Using these test cases, we construct preference pairs based on pass rates over sampled programs to train reward models with Bradley-Terry loss. It shows an average of 10-point improvement for Llama-3.1-8B-Ins and 5-point improvement for Qwen2.5-Coder-7B-Ins through best-of-32 sampling, making the 7B model on par with 236B DeepSeek-V2.5. Furthermore, we conduct reinforcement learning with both reward models and test-case pass rewards, leading to consistent improvements across HumanEval, MBPP, BigCodeBench, and LiveCodeBench (V4). Notably, we follow the R1-style training to start from Qwen2.5-Coder-base directly and show that our RL training can improve model on HumanEval-plus by over 25% and MBPP-plus by 6% for merely 80 optimization steps. We believe our results highlight the huge potential of reinforcement learning in coder models.

ACL 2025

ACECODER: Acing Coder RL via Automated Test-Case Synthesis

coding llm

reward model

reinforcement learning

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AAAI 2026

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

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### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Message from the General Chair: 
*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025. ACL2025continues our field’s tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.* 
[Read more](https://drive.google.com/file/d/1GI_hvOpjswAuYdUTromfeDiPpCcqidwg/view?usp=sharing)

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Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Chain-of-Thought (CoT) prompting has become the de facto method to elicit reasoning capabilities from large language models (LLMs). However, to mitigate hallucinations in CoT that are notoriously difficult to detect, current methods such as process reward models (PRMs) or self-consistency operate as opaque boxes and do not provide checkable evidence for their judgments, possibly limiting their effectiveness. To address this issue, we draw inspiration from the idea that "the gold standard for supporting a mathematical claim is to provide a proof". We propose a retrospective, step-aware formal verification framework Safe. Rather than assigning arbitrary scores, we strive to articulate mathematical claims in formal mathematical language Lean 4 at each reasoning step and provide formal proofs to identify hallucinations. We evaluate our framework Safe across multiple language models and various mathematical datasets, demonstrating a significant performance improvement while offering interpretable and verifiable evidence. We also propose FormalStep as a benchmark for step correctness theorem proving with $30,809$ formal statements. To the best of our knowledge, our work represents the first endeavor to utilize formal mathematical language Lean 4 for verifying content generated by LLMs, aligning with the reason why formal mathematical languages were created in the first place: to provide a robust foundation for hallucination-prone human-written proofs.

Safe: Enhancing Mathematical Reasoning in Large Language Models via Retrospective Step-aware Formal Verification

Large language models (LLMs) have the potential to revolutionize smart home assistants by enhancing their ability to accurately understand user needs and respond appropriately, which is extremely beneficial for building a smarter home environment. While recent studies have explored integrating LLMs into smart home systems, they primarily focus on handling straightforward, valid single-device operation instructions. However, real-world scenarios are far more complex and often involve users issuing invalid instructions or controlling multiple devices simultaneously. These have two main challenges: LLMs must accurately identify and rectify errors in user instructions and execute multiple user instructions perfectly. To address these challenges and advance the development of LLM-based smart home assistants, we introduce HomeBench, the first smart home dataset with valid and invalid instructions across single and multiple devices in this paper. We have experimental results on 13 distinct LLMs; e.g., GPT-4o achieves only a 0.0\% success rate in the scenario of invalid multi-device instructions, revealing that the existing state-of-the-art LLMs still cannot perform well in this situation even with the help of in-context learning, retrieval-augmented generation, and fine-tuning. Our code and dataset are publicly available at https://github.com/BITHLP/HomeBench.

HomeBench: Evaluating LLMs in Smart Homes with Valid and Invalid Instructions Across Single and Multiple Devices

Iterative data generation and model retraining are widely used to align large language models (LLMs).
It typically involves a policy model to generate on-policy responses and a reward model to guide training data selection. 
Direct Preference Optimization (DPO) further enhances this process by constructing preference pairs of chosen and rejected responses. 
In this work, we aim to \emph{scale up} the number of on-policy samples via repeated random sampling to improve alignment performance. 
Conventional practice selects the sample with the highest reward as chosen and the lowest as rejected for DPO. 
However, our experiments reveal that this strategy leads to a \emph{decline} in performance as the sample size increases. 
To address this, we investigate preference data construction through the lens of underlying normal distribution of sample rewards. 
We categorize the reward space into seven representative points and systematically explore all 21 ($C_7^2$) pairwise combinations. 
Through evaluations on four models using AlpacaEval 2, we find that selecting the rejected response at reward position $\mu - 2\sigma$ rather than the minimum reward, is crucial for optimal performance. 
We finally introduce a scalable preference data construction strategy that consistently enhances model performance as the sample scale increases.

Finding the Sweet Spot: Preference Data Construction for Scaling Preference Optimization

The strong performance of large language models (LLMs) raises extensive discussion on their application to code generation. Recent research suggests continuous program refinements through visible tests to improve code generation accuracy in LLMs. However, these methods suffer from LLMs' inefficiency and limited reasoning capacity. In this work, we propose an LLM programming workflow (LPW) designed to improve both initial code generation and subsequent refinements within a structured two-phase workflow. Specifically, the solution generation phase formulates a solution plan, which is then verified through visible tests to specify the intended natural language solution. Subsequently, the code implementation phase drafts an initial code according to the solution plan and its verification. If the generated code fails the visible tests, the plan verification serves as the intended solution to consistently inform the refinement process for correcting bugs. Compared to state-of-the-art methods across various existing LLMs, LPW significantly improves the Pass@1 accuracy by up to 16.4% on well-established text-to-code generation benchmarks. LPW also sets new state-of-the-art Pass@1 accuracy, achieving 98.2% on HumanEval, 84.8% on MBPP, 59.3% on LiveCode, 62.6% on APPS, and 34.7% on CodeContests, using GPT-4o as the backbone. Our code is publicly available at: https://github.com/you68681/lpw.

Planning-Driven Programming: A Large Language Model Programming Workflow

Large language models (LLMs) with one or more fine-tuning phases have become necessary to unlock various capabilities, enabling LLMs to follow natural language instructions and align with human preferences. However, it carries the risk of catastrophic forgetting during sequential training, the parametric knowledge or the ability learned in previous stages may be overwhelmed by incoming training data. This paper finds that LLMs can restore some original knowledge by regularly resetting partial parameters. Inspired by this, we introduce \underline{H}alf \underline{F}ine-\underline{T}uning (HFT) for LLMs, as a substitute for full fine-tuning (FFT), to mitigate the forgetting issues, where half of the parameters are selected to learn new tasks. In contrast, the other half are frozen to retain previous knowledge. We provide a feasibility analysis from the optimization perspective and interpret the parameter selection operation as a regularization term. HFT could be seamlessly integrated into existing fine-tuning frameworks without changing the model architecture. Extensive experiments and analysis on supervised fine-tuning, direct preference optimization, and continual learning consistently demonstrate the effectiveness, robustness, and efficiency of HFT. Compared with FFT, HFT not only significantly alleviates the forgetting problem, but also achieves the best performance in a series of downstream benchmarks, with an approximately 30% reduction in training time.

HFT: Half Fine-Tuning for Large Language Models

Commonsense question answering (QA) are widely used to evaluate the commonsense abilities of large language models. However, answering commonsense questions correctly requires not only knowledge but also reasoning—even for seemingly simple questions. We demonstrate that such hidden reasoning attributes in commonsense questions can lead evaluation accuracy differences of up to 24.8% across different difficulty levels in the same benchmark. Current benchmarks overlook these hidden reasoning attributes, making it difficult to assess a model's specific levels of commonsense knowledge and reasoning ability. To address this issue, we introduce ReComSBench, a novel framework that reveals hidden reasoning attributes behind commonsense questions by leveraging the knowledge generated during the reasoning process. Additionally, ReComSBench proposes three new metrics for decoupled evaluation: Knowledge Balanced Accuracy, Marginal Sampling Gain, and Knowledge Coverage Ratio. Experiments show that ReComSBench provides insights into model performance that traditional benchmarks cannot offer. The difficulty stratification based on revealed hidden reasoning attributes performs as effectively as the model-probability-based approach but is more generalizable and better suited for improving a model's commonsense reasoning abilities. By uncovering and analyzing the hidden reasoning attributes in commonsense data, ReComSBench offers a new approach to enhancing existing commonsense benchmarks.

Beyond Surface Simplicity: Revealing Hidden Reasoning Attributes for Precise Commonsense Diagnosis

Mixture-of-Experts (MoE) shines brightly in large language models (LLMs) and demonstrates outstanding performance in plentiful natural language processing tasks. However, existing methods transforming LLMs from dense to MoE face significant data requirements and typically rely on large-scale post-training. In this paper, we propose Upcycling Instruction Tuning (UpIT), a data-efficient approach for tuning a dense pre-trained model into a MoE instruction model. Specifically, we first point out that intermediate checkpoints during instruction tuning of the dense model are naturally suitable for specialized experts, and then propose an expert expansion stage to flexibly achieve models with flexible numbers of experts, where genetic algorithm and parameter merging are introduced to ensure sufficient diversity of new extended experts. To ensure that each specialized expert in the MoE model works as expected, we select a small amount of seed data that each expert excels to pre-optimize the router. Extensive experiments with various data scales and upcycling settings demonstrate the outstanding performance and data efficiency of UpIT, as well as stable improvement in expert or data scaling. Further analysis reveals the importance of ensuring expert diversity in upcycling.

Upcycling Instruction Tuning from Dense to Mixture-of-Experts via Parameter Merging

The study of machine learning-based logical query-answering enables reasoning with large-scale and incomplete knowledge graphs. This paper further advances this line of research by considering the uncertainty in the knowledge. The uncertain nature of knowledge is widely observed in the real world, but does not align seamlessly with the first-order logic underpinning existing studies. To bridge this gap, we study the setting of soft queries on uncertain knowledge, which is motivated by the establishment of soft constraint programming. We further propose an ML-based approach with both forward inference and backward calibration to answer soft queries on large-scale, incomplete, and uncertain knowledge graphs. Theoretical discussions reveal that our method ensures there are no catastrophic cascading errors in our forward inference algorithm while maintaining the same complexity as state-of-the-art inference algorithms for first-order queries. Empirical results justify the superior performance of our approach against previous ML-based methods with number embedding extensions.

Extending Complex Logical Queries on Uncertain Knowledge Graphs

As large language models (LLMs) require continuous knowledge updates and the mitigation of hallucination issues in generated content, lifelong model editing has become a prominent research area. A mainstream knowledge editing method usually freezes LLM's original parameters and adds extra trainable modules for new knowledge management, reducing interference with old knowledge. Although these approaches have achieved some success, our experiments show that, after extensive editing, the model's knowledge understanding and memory capacity significantly degrade, particularly concerning early edited knowledge. The root cause is that subsequent edits interfere with the previously edited knowledge, and we refer to this phenomenon as knowledge coupling. To address this issue, we propose the \textbf{\underline{K}nowledge \underline{D}ecoupling \underline{E}diting} (KDE) method. Specifically, KDE stores the basis vectors of the representation space of past edits in a knowledge cache. It projects the gradient of the current edit onto a space orthogonal to previous knowledge for updating. This method effectively alleviates the coupling between different pieces of knowledge. We also propose a two-stage training strategy to better balance the model's ability to edit new knowledge and distinguish whether a query is related to previous edits. This strategy gradually reduces the interference between new knowledge editing and query distinction, maintaining stable performance during long-term editing. We compared KDE with nine cutting-edge editing methods across multiple mainstream LLMs. The results demonstrate that, regarding question-answering ability and hallucination mitigation, KDE achieves average improvements of 14% and 61%.

Knowledge Decoupling via Orthogonal Projection for Lifelong Editing of Large Language Models

Inference-time optimization scales computation to derive deliberate reasoning steps for effective performance. While previous search-based strategies address the short-sightedness of auto-regressive generation, the vast search space leads to excessive exploration and insufficient exploitation. To strike an efficient balance to derive the optimal step, we frame the decoding strategy as foresight sampling, leveraging simulated future steps to obtain globally optimal step estimation. Built on it, we propose a novel decoding strategy, named 
$\phi$-Decoding. To provide a precise and expressive estimation of step value, 
$\phi$-Decoding approximates two distributions via foresight and clustering. Sampling from the joint distribution, the optimal steps can be selected for exploitation. To support adaptive computation allocation, we propose in-width and in-depth pruning strategies, featuring a light-weight solution to achieve inference efficiency. Extensive experiments across seven benchmarks show 
$\phi$-Decoding outperforms strong baselines in both performance and efficiency. Additional analysis demonstrates its generalization across various LLMs and scalability across a wide range of computing budgets.

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Safe: Enhancing Mathematical Reasoning in Large Language Models via Retrospective Step-aware Formal Verification

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