While Large Language Models (LLMs) demonstrate remarkable zero-shot generalization, adapting them to downstream tasks or shifting data distributions often requires continual fine-tuning—a process prone to catastrophic forgetting and limited knowledge transfer. This challenge is especially pronounced in online Incremental Learning (IL) settings, where task boundaries are blurred, and data arrives in a non-stationary stream. To address these issues, we propose GROLE (Group Relative Optimization for LoRA Experts), a novel approach that incrementally constructs a pool of frozen, task-specific Low-Rank Adaptation (LoRA) experts. At its core, GROLE employs a lightweight, instance-level expert selector optimized through a group relative reinforcement learning objective, which dynamically combines relevant experts to maximize adaptability without compromising stability. Extensive experiments across diverse incremental learning benchmarks show that GROLE consistently outperforms state-of-the-art methods, particularly in task-free and blurred-boundary settings, achieving an optimal balance between plasticity and robustness.

ESPnet-ST-v2 is a revamp of the open-source ESPnet-ST toolkit necessitated by the broadening interests of the spoken language translation community. ESPnet-ST-v2 supports 1) offline speech-to-text translation (ST), 2) simultaneous speech-to-text translation (SST), and 3) offline speech-to-speech translation (S2ST) – each task is supported with a wide variety of approaches, differentiating ESPnet-ST-v2 from other open source spoken language translation toolkits. This toolkit offers state-of-the-art architectures such as transducers, hybrid CTC/attention, multi-decoders with searchable intermediates, time-synchronous blockwise CTC/attention, Translatotron models, and direct discrete unit models. In this paper, we describe the overall design, example models for each task, and performance benchmarking behind ESPnet-ST-v2, which is publicly available at https://github.com/espnet/espnet.

Towards developing high-performing ASR for low-resource languages, approaches to address the lack of resources are to make use of data from multiple languages, and to augment the training data by creating acoustic variations. In this work we present a single grapheme-based ASR model learned on 7 geographically proximal languages, using standard hybrid BLSTM-HMM acoustic models with lattice-free MMI objective. We build the single ASR grapheme set via taking the union over each language-specific grapheme set, and we find such multilingual graphemic hybrid ASR model can perform language-independent recognition on all 7 languages, and substantially outperform each monolingual ASR model. Secondly, we evaluate the efficacy of multiple data augmentation alternatives within language, as well as their complementarity with multilingual modeling. Overall, we show that the proposed multilingual graphemic hybrid ASR with various data augmentation can not only recognize any within training set languages, but also provide large ASR performance improvements.