SLMFix is a code‑generation pipeline that uses a small language model fine‑tuned with reinforcement learning to correct syntactic errors in programs produced by large language models for domain‑specific languages. The approach relies on interpreter feedback to guide the error‑fixing process. Experiments show that SLMFix improves validator pass rates by 40% on low‑resource programming languages and removes over 50% of syntactic errors on high‑resource DSLs, outperforming supervised fine‑tuning even for 7B models.
By David Jiahao Fu, Aryan Gupta, Aaron Councilman, Yu-Xiong Wang, Vikram Adve
arXiv:2509. 23982v2 Announce Type: replace-cross Abstract: Preference alignment is a critical step in making Large Language Models (LLMs) useful and aligned with (human) preferences.
By Lucio La Cava, Andrea Tagarelli
Composition‑RL is an offline data‑augmentation method that composes existing prompts before reinforcement learning to address the pass‑rate‑1 bottleneck in large‑language‑model training. By generating and verifying a composed dataset once from a fixed source pool, it avoids modifying the RL optimizer and improves reasoning performance across models ranging from 4B to 30B. A curriculum variant that gradually increases compositional depth further boosts performance, and the approach also facilitates cross‑domain RL by combining prompts from different domains.
By Xin Xu, Clive Bai, Kai Yang, Tianhao Chen, Cai Yuchen, Yangkun Chen, Weijie Liu, Hao Chen, Yang Wang, Saiyong Yang, Can Yang
arXiv:2512. 03086v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown remarkable capabilities in code translation, yet their performance deteriorates in low-resource programming domains such as Fortran and emerging frameworks like CUDA, where high-quality parallel data are scarce.
By Le Chen, Nuo Xu, Winson Chen, Bin Lei, Pei-Hung Lin, Dunzhi Zhou, Rajeev Thakur, Caiwen Ding, Ali Jannesari, Chunhua Liao
ORCA is a new benchmark for evaluating large language models on Data Science Code Translation (DSCT), comprising two settings: ORCA-MAIN with 1,600 grounding-level tasks across data querying, manipulation, and deep learning, and ORCA-PROJECT with 200 full-project translation tasks across seven data‑science task types. Each task includes reference translations and test cases to verify functional equivalence, and a multi‑stage quality verification process ensures task correctness. Experiments show that even state‑of‑the‑art LLMs perform poorly on DSCT, with Claude‑Opus‑4.6 achieving only 56.92% success on ORCA‑MAIN and 33.67% on ORCA‑PROJECT, while an intent‑augmented approach improves success rates by 4.80% and 5.33% respectively.
By Xiaolong Li, Jinyang Li, Bowen Qin, Ge Qu, Nan Huo, Xiaohan Xu, Shipei Lin, Reynold Cheng
arXiv:2608. 11715v1 Announce Type: cross Abstract: The reliability of Large Language Models (LLMs) for API calling degrades in multilingual settings.
By Siddharth Chauhan, Thomas Butler, Abhishek Singhania, Pankaj Porwal, Honey Gupta