71 citations · 130 across the 47 of their papers we have counts for
61 papers
Reconstruct, Practice, Go Real: Guided Self-Improvement for Embodied Agents
Yen-Jen Wang, Haozhe Jiang, Shuying Deng +7
Building reliable robot capabilities across diverse tasks requires substantial human effort to develop and maintain skills, design rewards, and integrate perception with control. W…
Inference Auctions
Keegan Harris, Siddharth Prasad, Asher Trockman +2
When inference demand exceeds available compute capacity, model providers must decide which requests should be served first. Users have different tolerances for delay from an LLM A…
Learn Your Own Thoughts: Abstract Token Curriculum
Khashayar Gatmiry, Avrajit Ghosh, Parsa Mirtaheri +4
Large Language Models (LLMs) have achieved remarkable reasoning capabilities by utilizing chain-of-thought (CoT) as a scratchpad for intermediate stages of thinking. However, CoT t…
Distortion of AI Alignment Revisited: RLHF is a Decent Utilitarian Aligner
Kazusato Oko, Annie Ulichney, Nika Haghtalab +1
While Reinforcement Learning from Human Feedback (RLHF) is the standard paradigm for aligning large language models with human preferences, its effectiveness in pluralistic setting…
Computationally Efficient Collaborative Communication Via Regularity-Based Coarsening
Mark Bedaywi, Scott Emmons, Nika Haghtalab +1
Our results show that the existence of a short high-utility protocol already suffices for efficient communication. In particular, in a game with possible observations and a…
Post-Training at the Edge of Detectability: A Game-Theoretic Approach to Fine-Tuning
Keegan Harris, Brian W. Lee, Ian Waudby-Smith +3
Reinforcement learning (RL) fine-tuning is widely used in language model training to improve performance on a target task while limiting drift from a reference policy. A standard w…
Provably Optimal Learning Algorithms for Assistance Games
Nivasini Ananthakrishnan, Mark Bedaywi, Michael I. Jordan +2
This paper studies an online variant of the assistance games framework, where an informed agent and an uninformed agent repeatedly interact over timesteps to optimize a common…
Blackwell Approachability and Gradient Equilibrium are Equivalent
Brian W. Lee, Nika Haghtalab, Michael I. Jordan +1
Gradient equilibrium (GEQ) is a recently introduced online optimization framework that generalizes first-order stationarity from offline optimization and abstracts problems like on…
Strategic Feature Selection
Jivat Neet Kaur, Pratik Patil, Divya Shanmugam +6
When algorithmic predictors inform resource allocation in high-stakes domains such as healthcare, these predictors must account for strategic manipulation of input features. The ty…
DiPOD: Diffusion Policy Optimization without Drifting Apart
Haozhe Jiang, Haiwen Feng, Pieter Abbeel +3
RL post-training has become increasingly pivotal for improving diffusion policies, but existing diffusion policy-gradient methods are often unstable and cannot achieve reliable pol…
Three Years of r/ChatGPT: Societal Impact Evaluations from Social Media Data
Jessica Dai, Sean Garcia, Emma Pierson +2
ChatGPT was launched on November 30, 2022; the r/ChatGPT subreddit was created just one day later. Since then, chatbot-based AI products have gone from niche proofs-of-concept to w…
Pluralistic Leaderboards
Nika Haghtalab, Ariel D. Procaccia, Han Shao +2
Recent leaderboard-based evaluations of large language models aggregate user feedback by fitting a Bradley--Terry model to pairwise comparisons, producing a single global ranking b…
Subliminal Effects in Your Data: A General Mechanism via Log-Linearity
Ishaq Aden-Ali, Noah Golowich, Allen Liu +3
Training modern large language models (LLMs) has become a veritable smorgasbord of algorithms and datasets designed to elicit particular behaviors, making it critical to develop te…
Diffusion Language Models are Provably Optimal Parallel Samplers
Haozhe Jiang, Nika Haghtalab, Lijie Chen
Diffusion language models (DLMs) have emerged as a promising alternative to autoregressive models for faster inference via parallel token generation. We provide a rigorous foundati…
Sample-Adaptivity Tradeoff in On-Demand Sampling
Nika Haghtalab, Omar Montasser, Mingda Qiao
We study the tradeoff between sample complexity and round complexity in on-demand sampling, where the learning algorithm adaptively samples from distributions over a limited nu…
Panprediction: Optimal Predictions for Any Downstream Task and Loss
Sivaraman Balakrishnan, Nika Haghtalab, Daniel Hsu +2
Supervised learning is classically formulated as training a model to minimize a fixed loss function over a fixed distribution, or task. However, an emerging paradigm instead views…
On Surjectivity of Neural Networks: Can you elicit any behavior from your model?
Haozhe Jiang, Nika Haghtalab
Given a trained neural network, can any specified output be generated by some input? Equivalently, does the network correspond to a function that is surjective? In generative model…
Distortion of AI Alignment: Does Preference Optimization Optimize for Preferences?
Paul Gölz, Nika Haghtalab, Kunhe Yang
After pre-training, large language models are aligned with human preferences based on pairwise comparisons. State-of-the-art alignment methods (such as PPO-based RLHF and DPO) are…
X-Guard: Multilingual Guard Agent for Content Moderation
Bibek Upadhayay, Vahid Behzadan, Ph. D
Large Language Models (LLMs) have rapidly become integral to numerous applications in critical domains where reliability is paramount. Despite significant advances in safety framew…
From Style to Facts: Mapping the Boundaries of Knowledge Injection with Finetuning
Eric Zhao, Pranjal Awasthi, Nika Haghtalab
Finetuning provides a scalable and cost-effective means of customizing language models for specific tasks or response styles, with greater reliability than prompting or in-context…
Multi-Agent Risks from Advanced AI
Lewis Hammond, Alan Chan, Jesse Clifton +41
The rapid development of advanced AI agents and the imminent deployment of many instances of these agents will give rise to multi-agent systems of unprecedented complexity. These s…
Leakage-Robust Bayesian Persuasion
Nika Haghtalab, Mingda Qiao, Kunhe Yang
This paper introduces leakage-robust Bayesian persuasion. Situated between public Bayesian persuasion [KG11] (and its multi-receiver variants [CCG23, Xu20]) and private Bayesian pe…
Learning With Multi-Group Guarantees For Clusterable Subpopulations
Jessica Dai, Nika Haghtalab, Eric Zhao
A canonical desideratum for prediction problems is that performance guarantees should hold not just on average over the population, but also for meaningful subpopulations within th…
Algorithmic Content Selection and the Impact of User Disengagement
Emilio Calvano, Nika Haghtalab, Ellen Vitercik +1
Digital services face a fundamental trade-off in content selection: they must balance the immediate revenue gained from high-reward content against the long-term benefits of mainta…