CHAI for LLMs is a framework that improves large language models’ performance on code‑mixed translation tasks by using LLMs as annotators to create preference data, applying reinforcement learning from AI feedback, incorporating LLM‑generated domain knowledge for iterative refinement, and evaluating on real‑world datasets. The approach yields a 68.45% average win rate over state‑of‑the‑art open‑source models in human‑adjudicated tests. It demonstrates a scalable method to enhance code‑mixed language understanding in open‑source LLMs.
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.
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.
arXiv:2608. 11715v1 Announce Type: cross Abstract: The reliability of Large Language Models (LLMs) for API calling degrades in multilingual settings.
The paper explores using reinforcement learning to enhance automatic text simplification for low‑resource languages, focusing on Catalan. It introduces a new reward function that blends the SARI metric with penalty terms, and applies Group Relative Policy Optimization (GRPO) to fine‑tune the IberianLLM‑7B‑Instruct model on the ASSET dataset. Post‑training, the model shows improved simplification performance on two Catalan benchmarks and reduces prior negative behaviors, though cross‑lingual transfer from English, Spanish, and Catalan translations of ASSET does not yield significant gains on an out‑of‑domain benchmark.
arXiv:2606. 08011v1 Announce Type: cross Abstract: Although directly prompting off-the-shelf Large Language Models (LLMs) to generate meaning-preserving source rewrites can effectively enhance Machine Translation (MT) quality, doing so requires manually tuning prompts for different MT models.
arXiv:2602. 11715v2 Announce Type: replace Abstract: Diffusion large language models (dLLMs) have emerged as a compelling alternative to autoregressive (AR) LLMs, owing to their capacity for parallel token generation.
arXiv:2608. 10812v1 Announce Type: cross Abstract: We study reference-free post-training for multilingual machine translation with open large language models.
arXiv:2602. 10238v2 Announce Type: replace-cross Abstract: The growing size of Large Language Models (LLMs) makes efficient inference challenging, primarily due to the memory demands of the autoregressive Key-Value (KV) cache.
The paper investigates whether reinforcement‑learning post‑training of code‑generating large language models can be done entirely offline using existing datasets, avoiding costly online code generation and GPU‑CPU communication. Experiments show that a few hours of offline RL can substantially boost zero‑shot code generation performance across models from 0.5 B to 7 B parameters, though the magnitude of improvement differs by model family.
arXiv:2508. 10123v3 Announce Type: replace-cross Abstract: Advanced reasoning in LLMs on challenging domains like mathematical reasoning can be tackled using verifiable rewards based reinforced fine-tuning (ReFT).
arXiv:2607. 20456v1 Announce Type: cross Abstract: Large language models excel at code generation for mainstream programming languages but struggle with rare, domain-specific languages such as MiniZinc, a constraint modeling language for combinatorial problems.
DE‑Venus is a unified, data‑efficient framework for reinforcement learning with verifiable rewards (RLVR) tailored to large language models. It structures the RLVR lifecycle into three modules—Active Data Selection, Weak Supervision Construction, and Training‑Time Supervision Refinement—allowing method‑specific decisions to be expressed as dataset transitions or online transformations while maintaining distributed execution contracts. Experiments on public benchmarks and three business scenarios show that DE‑Venus can preserve or improve model quality using only 10% of labels or 13% of relevant data, and can cut convergence steps by 63%–75% in selected business configurations.