Tianxin Wei

Low-rank adapters (LoRAs) are a parameter-efficient finetuning technique that injects trainable low-rank matrices into pretrained models to adapt them to new tasks. Mixture-of-LoRAs models expand neural networks efficiently by routing each layer input to a small subset of specialized LoRAs of the layer. Existing Mixture-of-LoRAs routers assign a learned routing weight to each LoRA to enable end-to-end training of the router. Despite their empirical promise, we discover, both theoretically and empirically, that the routing weights often collapse to only one LoRA even when we activate $k>1$ LoRAs during finetuning. When one LoRA has a dominantly large weight, then the computation of the other $k-1$ LoRAs are essentially **wasted** because using $k>1$ would have similar accuracy to $k=1$. This essentially limits the number of effective LoRAs and thus severely hinders the realized expressive power of existing Mixture-of-LoRAs models. How can we address this critical weakness? In this work, we attribute this weakness to the nature of learnable routing weights and rethink the fundamental design of the router. To address this critical issue, we propose a simple yet effective router design that we call *Reinforcement Routing for Mixtures of LoRAs* (ReMix). Our key idea is using **non-learnable** routing weights to ensure all active LoRAs to be equally effective, with no single LoRA dominating the routing weights. However, such non-learnable routing weights make it infeasible to directly train routers via gradient descent. In response, we further propose an unbiased gradient estimator for the router and employ the reinforce leave-one-out (RLOO) technique to reduce the variance of the estimator. Our gradient estimator also enables to scale up training compute to boost the predictive performance of our ReMix. Extensive experiments demonstrate that our proposed ReMix significantly outperforms state-of-the-art parameter-efficient finetuning methods under a small number of activated parameters.

Large language models (LLMs) exhibit diverse strengths and weaknesses across tasks, motivating recent efforts to ensemble multiple models to harness their complementary capabilities, boosting test-time performance. While model diversity and capability are known to influence ensemble effectiveness, a persistent challenge in LLM ensembling arises from mismatched tokenizer vocabularies. Existing alignment strategies typically rely on token-level embeddings or string-level heuristics of tokens, overlooking the tokenizer priors embedded during LLM pretraining. Specifically, tokenizers such as Byte-Pair Encoding (BPE) and Unigram are constructed by statistically analyzing large pretraining corpora to identify frequent subword units, and they tokenize text using greedy or probabilistic algorithms that reflect these learned subword distributions. In this work, we propose a novel and remarkably simple Token Translation (ToT) method that explicitly leverages these tokenizer priors to bridge heterogeneous token spaces. Our method is lightweight, requiring only a few lines of code, pre-computable, and highly efficient at inference. To further enhance robustness, we incorporate token-level model uncertainty to dynamically reweight each model’s contribution during decoding. Extensive evaluations across diverse model combinations and tasks demonstrate that our method consistently outperforms existing ensembling baselines.

While structured pruning presents a highly effective pathway for accelerating Large Language Model (LLM) inference, existing methods frequently suffer from significant performance degradation and demand computationally retraining to recover capabilities. To overcome these barriers, we present NIRVANA, a novel, hardware-aware structured pruning framework designed to preserve both zero-shot performance and the optimization landscape for downstream fine-tuning. Departing from traditional loss-based heuristics, our approach evaluates structural importance through a first-order function-space saliency inspired by the Neural Tangent Kernel (NTK), effectively safeguarding the model's critical training dynamics. To prevent structural collapse at high compression rates, we introduce a global unit-ranking strategy coupled with an analytically derived allocation mechanism, which optimally balances the pruning aggressiveness between attention heads and MLP neurons. Furthermore, we eliminate the instability typically associated with random data sampling by employing a lightweight, KL-divergence-driven calibration data selection process. Extensive evaluations across Llama3, Qwen, and T5 architectures demonstrate that NIRVANA consistently establishes new state-of-the-art results on different benchmarks, providing a theoretically sound and practical approach to LLM compression.

Adapting large language models (LLMs) to a targeted task efficiently and effectively remains a fundamental challenge. Such adaptation often requires iteratively improving the model toward a targeted task, yet collecting high-quality human-labeled data to support this process is costly and difficult to scale. As a result, synthetic data generation has emerged as a flexible and scalable alternative. One straightforward approach is through an iterative generation–training loop, where candidate data are synthesized through an external generator, the model is updated using these data and the process is repeated over iterations. However, generated samples can be noisy, highly redundant, or even misaligned with the targeted task distribution. Training indiscriminately on such data can dilute useful learning signals and even degrade model performance. To address this, we introduce a refined paradigm, namely an iterative generation–selection–training loop, which incorporates a selection step prior to model updates. Building on this paradigm, we propose EvoSelect, a data-efficient framework to evolve LLM effectively. Given candidate samples produced by the data generator, EvoSelect selects training data by jointly modeling targeted task alignment and diversity. We estimate task relevance through optimal transport with proxy gradient representations, which quantifies how well candidate samples align with the targeted task distribution. To mitigate redundancy, we incorporate a diversification mechanism that promotes coverage of complementary training samples. By interleaving alignment and diversification, EvoSelect enables progressive LLM evolution toward targeted tasks. Extensive experiments on various benchmarks demonstrate that with either weak or strong data generators, EvoSelect consistently improves adaptation efficacy over existing data selection methods.