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Andrew Li

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10 papers

2 author rows

ICLR Conference 2025 Conference Paper

EmbedLLM: Learning Compact Representations of Large Language Models

Richard Zhuang
Tianhao Wu 0002
Zhaojin Wen
Andrew Li
Jiantao Jiao
Kannan Ramchandran

With hundreds of thousands of language models available on Huggingface today, efficiently evaluating and utilizing these models across various downstream tasks has become increasingly critical. Many existing methods repeatedly learn task-specific representations of Large Language Models (LLMs), which leads to inefficiencies in both time and computational resources. To address this, we propose EmbedLLM, a framework designed to learn compact vector representations of LLMs that facilitate downstream applications involving many models, such as model routing. We introduce an encoder-decoder approach for learning such embedding, along with a systematic framework to evaluate their effectiveness. Empirical results show that EmbedLLM outperforms prior methods in model routing. Additionally, we demonstrate that our method can forecast a model's performance on multiple benchmarks, without incurring additional inference cost. Extensive probing experiments validate that the learned embeddings capture key model characteristics, e.g. whether the model is specialized for coding tasks, even without being explicitly trained on them. We open source our dataset, code and embedder to facilitate further research and application.

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NeurIPS Conference 2025 Conference Paper

Ground-Compose-Reinforce: Grounding Language in Agentic Behaviours using Limited Data

Andrew Li
Toryn Klassen
Andrew Wang
Parand A. Alamdari
Sheila McIlraith

Grounding language in perception and action is a key challenge when building situated agents that can interact with humans, or other agents, via language. In the past, addressing this challenge has required manually designing the language grounding or curating massive datasets that associate language with the environment. We propose Ground-Compose-Reinforce, an end-to-end, neurosymbolic framework for training RL agents directly from high-level task specifications—without manually designed reward functions or other domain-specific oracles, and without massive datasets. These task specifications take the form of Reward Machines, automata-based representations that capture high-level task structure and are in some cases autoformalizable from natural language. Critically, we show that Reward Machines can be grounded using limited data by exploiting compositionality. Experiments in a custom Meta-World domain with only 350 labelled pretraining trajectories show that our framework faithfully elicits complex behaviours from high-level specifications—including behaviours that never appear in pretraining—while non-compositional approaches fail.

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NeurIPS Conference 2022 Conference Paper

Dynamic Pricing with Monotonicity Constraint under Unknown Parametric Demand Model

Su Jia
Andrew Li
R Ravi

We consider the Continuum Bandit problem where the goal is to find the optimal action under an unknown reward function, with an additional monotonicity constraint (or, "markdown" constraint) that requires that the action sequence be non-increasing. This problem faithfully models a natural single-product dynamic pricing problem, called "markdown pricing", where the objective is to adaptively reduce the price over a finite sales horizon to maximize expected revenues. Jia et al '21 and Chen '21 independently showed a tight $T^{3/4}$ regret bound over $T$ rounds under *minimal* assumptions of unimodality and Lipschitzness in the reward (or, "revenue") function. This bound shows that the demand learning in markdown pricing is harder than unconstrained (i. e. , without the monotonicity constraint) pricing under unknown demand which suffers regret only of the order of $T^{2/3}$ under the same assumptions (Kleinberg '04). However, in practice the demand functions are usually assumed to have certain functional forms (e. g. linear or exponential), rendering the demand-learning easier and suggesting lower regret bounds. We investigate two fundamental questions, assuming the underlying demand curve comes from a given parametric family: (1) Can we improve the $T^{3/4}$ regret bound for markdown pricing, under extra assumptions on the functional forms of the demand functions? (2) Is markdown pricing still harder than unconstrained pricing, under these additional assumptions? To answer these, we introduce a concept called markdown dimension that measures the complexity of the parametric family and present tight regret bounds under this framework, thereby completely settling the aforementioned questions.

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NeurIPS Conference 2022 Conference Paper

Learning to Follow Instructions in Text-Based Games

Mathieu Tuli
Andrew Li
Pashootan Vaezipoor
Toryn Klassen
Scott Sanner
Sheila McIlraith

Text-based games present a unique class of sequential decision making problem in which agents interact with a partially observable, simulated environment via actions and observations conveyed through natural language. Such observations typically include instructions that, in a reinforcement learning (RL) setting, can directly or indirectly guide a player towards completing reward-worthy tasks. In this work, we study the ability of RL agents to follow such instructions. We conduct experiments that show that the performance of state-of-the-art text-based game agents is largely unaffected by the presence or absence of such instructions, and that these agents are typically unable to execute tasks to completion. To further study and address the task of instruction following, we equip RL agents with an internal structured representation of natural language instructions in the form of Linear Temporal Logic (LTL), a formal language that is increasingly used for temporally extended reward specification in RL. Our framework both supports and highlights the benefit of understanding the temporal semantics of instructions and in measuring progress towards achievement of such a temporally extended behaviour. Experiments with 500+ games in TextWorld demonstrate the superior performance of our approach.

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NeurIPS Conference 2022 Conference Paper

Markovian Interference in Experiments

Vivek Farias
Andrew Li
Tianyi Peng
Andrew Zheng

We consider experiments in dynamical systems where interventions on some experimental units impact other units through a limiting constraint (such as a limited supply of products). Despite outsize practical importance, the best estimators for this `Markovian' interference problem are largely heuristic in nature, and their bias is not well understood. We formalize the problem of inference in such experiments as one of policy evaluation. Off-policy estimators, while unbiased, apparently incur a large penalty in variance relative to state-of-the-art heuristics. We introduce an on-policy estimator: the Differences-In-Q's (DQ) estimator. We show that the DQ estimator can in general have exponentially smaller variance than off-policy evaluation. At the same time, its bias is second order in the impact of the intervention. This yields a striking bias-variance tradeoff so that the DQ estimator effectively dominates state-of-the-art alternatives. From a theoretical perspective, we introduce three separate novel techniques that are of independent interest in the theory of Reinforcement Learning (RL). Our empirical evaluation includes a set of experiments on a city-scale ride-hailing simulator.

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AAAI Conference 2021 Conference Paper

Disposable Linear Bandits for Online Recommendations

Melda Korkut
Andrew Li

We study the classic stochastic linear bandit problem under the restriction that each arm may be selected for limited number of times. This simple constraint, which we call disposability, captures a common restriction that occurs in recommendation problems from a diverse array of applications ranging from personalized styling services to dating platforms. We show that the regret for this problem is characterized by a previously-unstudied function of the reward distribution among optimal arms. Algorithmically, our upper bound relies on an optimism-based policy which, while computationally intractable, lends itself to approximation via a fast alternating heuristic initialized with a classic similarity score. Experiments show that our policy dominates a set of benchmarks which includes algorithms known to be optimal for the linear bandit without disposability, along with natural modifications to these algorithms for the disposable setting.

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NeurIPS Conference 2021 Conference Paper

Greedy Approximation Algorithms for Active Sequential Hypothesis Testing

Kyra Gan
Su Jia
Andrew Li

In the problem of \emph{active sequential hypothesis testing} (ASHT), a learner seeks to identify the \emph{true} hypothesis from among a known set of hypotheses. The learner is given a set of actions and knows the random distribution of the outcome of any action under any true hypothesis. Given a target error $\delta>0$, the goal is to sequentially select the fewest number of actions so as to identify the true hypothesis with probability at least $1 - \delta$. Motivated by applications in which the number of hypotheses or actions is massive (e. g. , genomics-based cancer detection), we propose efficient (greedy, in fact) algorithms and provide the first approximation guarantees for ASHT, under two types of adaptivity. Both of our guarantees are independent of the number of actions and logarithmic in the number of hypotheses. We numerically evaluate the performance of our algorithms using both synthetic and real-world DNA mutation data, demonstrating that our algorithms outperform previously proposed heuristic policies by large margins.

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NeurIPS Conference 2021 Conference Paper

Learning Treatment Effects in Panels with General Intervention Patterns

Vivek Farias
Andrew Li
Tianyi Peng

The problem of causal inference with panel data is a central econometric question. The following is a fundamental version of this problem: Let $M^*$ be a low rank matrix and $E$ be a zero-mean noise matrix. For a `treatment' matrix $Z$ with entries in $\{0, 1\}$ we observe the matrix $O$ with entries $O_{ij}: = M^*_{ij} + E_{ij} + \mathcal{T}_{ij} Z_{ij}$ where $\mathcal{T}_{ij} $ are unknown, heterogenous treatment effects. The problem requires we estimate the average treatment effect $\tau^*: = \sum_{ij} \mathcal{T}_{ij} Z_{ij} / \sum_{ij} Z_{ij}$. The synthetic control paradigm provides an approach to estimating $\tau^*$ when $Z$ places support on a single row. This paper extends that framework to allow rate-optimal recovery of $\tau^*$ for general $Z$, thus broadly expanding its applicability. Our guarantees are the first of their type in this general setting. Computational experiments on synthetic and real-world data show a substantial advantage over competing estimators.

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ICRA Conference 2019 Conference Paper

Learning Deep Visuomotor Policies for Dexterous Hand Manipulation

Divye Jain
Andrew Li
Shivam Singhal
Aravind Rajeswaran
Vikash Kumar
Emanuel Todorov

Multi-fingered dexterous hands are versatile and capable of acquiring a diverse set of skills such as grasping, in-hand manipulation, and tool use. To fully utilize their versatility in real-world scenarios, we require algorithms and policies that can control them using on-board sensing capabilities, without relying on external tracking or motion capture systems. Cameras and tactile sensors are the most widely used on-board sensors that do not require instrumentation of the world. In this work, we demonstrate an imitation learning based approach to train deep visuomotor policies for a variety of manipulation tasks with a simulated five fingered dexterous hand. These policies directly control the hand using high dimensional visual observations of the world and propreoceptive observations from the robot, and can be trained efficiently with a few hundred expert demonstration trajectories. We also find that using touch sensing information enables faster learning and better asymptotic performance for tasks with high degree of occlusions. Video demonstration of our results are available at: https://sites.google.com/view/hand-vil/

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ICRA Conference 2019 Conference Paper

Segmenting Unknown 3D Objects from Real Depth Images using Mask R-CNN Trained on Synthetic Data

Michael Danielczuk
Matthew Matl
Saurabh Gupta 0001
Andrew Li
Andrew Lee 0002
Jeffrey Mahler
Ken Goldberg

The ability to segment unknown objects in depth images has potential to enhance robot skills in grasping and object tracking. Recent computer vision research has demonstrated that Mask R-CNN can be trained to segment specific categories of objects in RGB images when massive hand-labeled datasets are available. As generating these datasets is time-consuming, we instead train with synthetic depth images. Many robots now use depth sensors, and recent results suggest training on synthetic depth data can transfer successfully to the real world. We present a method for automated dataset generation and rapidly generate a synthetic training dataset of 50, 000 depth images and 320, 000 object masks using simulated heaps of 3D CAD models. We train a variant of Mask R-CNN with domain randomization on the generated dataset to perform category-agnostic instance segmentation without any hand-labeled data and we evaluate the trained network, which we refer to as Synthetic Depth (SD) Mask R-CNN, on a set of real, high-resolution depth images of challenging, densely-cluttered bins containing objects with highly-varied geometry. SD Mask R-CNN outperforms point cloud clustering baselines by an absolute 15% in Average Precision and 20% in Average Recall on COCO benchmarks, and achieves performance levels similar to a Mask R-CNN trained on a massive, hand-labeled RGB dataset and fine-tuned on real images from the experimental setup. We deploy the model in an instance-specific grasping pipeline to demonstrate its usefulness in a robotics application. Code, the synthetic training dataset, and supplementary material are available at https://bit.ly/2letCuE.

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