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Peiran Yu

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5 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

Bilevel ZOFO: Efficient LLM Fine-Tuning and Meta-Training

Reza Shirkavand
Peiran Yu
Qi He
Heng Huang

Fine-tuning pre-trained Large Language Models (LLMs) for downstream tasks using First-Order (FO) optimizers presents significant computational challenges. Parameter-Efficient Fine-Tuning~(PEFT) methods have been proposed to address these challenges by freezing most model parameters and training only a small subset. While PEFT is efficient, it may not outperform full fine-tuning when high task-specific performance is required. Zeroth-Order (ZO) methods offer an alternative for fine-tuning the entire pre-trained model by approximating gradients using only the forward pass, thus eliminating the computational burden of back-propagation, % in first-order methods, but they converge painfully slowly and are very sensitive to the choice of task prompts. We bridge these worlds with Bilevel‑ZOFO, a penalty‑based bilevel formulation that treats adapter parameters as a lower‑level learner coupled to an upper‑level ZO optimizer of the full backbone. This double-loop optimization strategy only requires the gradient of the PEFT model and the forward pass of the base model. We provide theoretical convergence guarantees for Bilevel ZOFO. Empirically, we demonstrate that Bilevel-ZOFO significantly outperforms existing ZO methods, achieves 2–4$\times$ faster training, and reduces sensitivity to prompts. Bilevel-ZOFO also outperforms FO PEFT methods while maintaining similar memory efficiency. Additionally, we show its strong potential for meta learning.

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NeurIPS Conference 2025 Conference Paper

Cost-Aware Contrastive Routing for LLMs

Reza Shirkavand
Shangqian Gao
Peiran Yu
Heng Huang

We study cost-aware routing for large language models across diverse and dynamic pools of models. Existing approaches often overlook prompt-specific context, rely on expensive model profiling, assume a fixed set of experts, or use inefficient trial-and-error strategies. We introduce Cost-Spectrum Contrastive Routing (CSCR), a lightweight framework that maps both prompts and models into a shared embedding space to enable fast, cost-sensitive selection. CSCR uses compact, fast-to-compute logit footprints for open-source models and perplexity fingerprints for black-box APIs. A contrastive encoder is trained to favor the cheapest accurate expert within adaptive cost bands. At inference time, routing reduces to a single $k$‑NN lookup via a FAISS index, requiring no retraining when the expert pool changes and enabling microsecond latency. Across multiple benchmarks, CSCR consistently outperforms baselines, improving the accuracy–cost tradeoff by up to 25\%, while generalizing robustly to unseen LLMs and out-of-distribution prompts.

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ICML Conference 2025 Conference Paper

Revisiting Convergence: Shuffling Complexity Beyond Lipschitz Smoothness

Qi He
Peiran Yu
Ziyi Chen 0002
Heng Huang 0001

Shuffling-type gradient methods are favored in practice for their simplicity and rapid empirical performance. Despite extensive development of convergence guarantees under various assumptions in recent years, most require the Lipschitz smoothness condition, which is often not met in common machine learning models. We highlight this issue with specific counterexamples. To address this gap, we revisit the convergence rates of shuffling-type gradient methods without assuming Lipschitz smoothness. Using our stepsize strategy, the shuffling-type gradient algorithm not only converges under weaker assumptions but also match the current best-known convergence rates, thereby broadening its applicability. We prove the convergence rates for nonconvex, strongly convex, and non-strongly convex cases, each under both random reshuffling and arbitrary shuffling schemes, under a general bounded variance condition. Numerical experiments further validate the performance of our shuffling-type gradient algorithm, underscoring its practical efficacy.

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ICLR Conference 2024 Conference Paper

Dropout Enhanced Bilevel Training

Peiran Yu
Junyi Li 0002
Heng Huang 0001

Bilevel optimization problems appear in many widely used machine learning tasks. Bilevel optimization models are sensitive to small changes, and bilevel training tasks typically involve limited datasets. Therefore, overfitting is a common challenge in bilevel training tasks. This paper considers the use of dropout to address this problem. We propose a bilevel optimization model that depends on the distribution of dropout masks. We investigate how the dropout rate affects the hypergradient of this model. We propose a dropout bilevel method to solve the dropout bilevel optimization model. Subsequently, we analyze the resulting dropout bilevel method from an optimization perspective. Analyzing the optimization properties of methods with dropout is essential because it provides convergence guarantees for methods using dropout. However, there has been limited investigation in this research direction. We provide the complexity of the resulting dropout bilevel method in terms of reaching an $\epsilon$ stationary point of the proposed stochastic bilevel model. Empirically, we demonstrate that overfitting occurs in data cleaning problems, and the method proposed in this work mitigates this issue.

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TMLR Journal 2024 Journal Article

Hessian Free Efficient Single Loop Iterative Differentiation Methods for Bi-Level Optimization Problems

Peiran Yu
Junyi Li
Heng Huang

Bilevel optimization problems have been actively studied in recent machine learning research due to their broad applications. In this work, we investigate single-loop methods with iterative differentiation (ITD) for nonconvex bilevel optimization problems. For deterministic bilevel problems, we propose an efficient single-loop ITD-type method (ES-ITDM). Our method employs historical updates to approximate the hypergradient. More importantly, based on ES-ITDM, we propose a new method that avoids computing Hessians. This Hessian-free method requires fewer backpropagations and thus has a lower computational cost. We analyze the convergence properties of the proposed methods in two aspects. We provide the convergence rates of the sequences generated by ES-ITD based on the Kurdyka-\L ojasiewicz (KL) property. We also show that the Hessian-free stochastic ES-ITDM has the best-known complexity while has cheaper computation. The empirical studies show that our Hessian-free stochastic variant is more efficient than existing Hessian-free methods and other state-of-the-art bilevel optimization approaches.

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