Publications

Motif: Intrinsic Motivation From Artificial Intelligence Feedback
Martin Klissarov
Pierluca D'Oro
Shagun Sodhani
Roberta Raileanu
Pierre-Luc Bacon
Pascal Vincent
Amy Zhang
Mikael Henaff
Exploring rich environments and evaluating one's actions without prior knowledge is immensely challenging. In this paper, we propose Motif, … (voir plus)a general method to interface such prior knowledge from a Large Language Model (LLM) with an agent. Motif is based on the idea of grounding LLMs for decision-making without requiring them to interact with the environment: it elicits preferences from an LLM over pairs of captions to construct an intrinsic reward, which is then used to train agents with reinforcement learning. We evaluate Motif's performance and behavior on the challenging, open-ended and procedurally-generated NetHack game. Surprisingly, by only learning to maximize its intrinsic reward, Motif achieves a higher game score than an algorithm directly trained to maximize the score itself. When combining Motif's intrinsic reward with the environment reward, our method significantly outperforms existing approaches and makes progress on tasks where no advancements have ever been made without demonstrations. Finally, we show that Motif mostly generates intuitive human-aligned behaviors which can be steered easily through prompt modifications, while scaling well with the LLM size and the amount of information given in the prompt.
2024-01-15
ICLR.cc/2024/Conference (poster)
doi.org
openreview.net
Object-Centric Architectures Enable Efficient Causal Representation Learning
Amin Mansouri
Jason Hartford
Yan Zhang
Yoshua Bengio
Causal representation learning has showed a variety of settings in which we can disentangle latent variables with identifiability guarantees… (voir plus) (up to some reasonable equivalence class). Common to all of these approaches is the assumption that (1) the latent variables are represented as
2024-01-15
ICLR.cc/2024/Conference (poster)
doi.org
openreview.net
PhyloGFN: Phylogenetic Inference with Generative Flow Networks
Mingyang Zhou
Zichao Yan
Elliot Layne
Nikolay Malkin
Dinghuai Zhang
Moksh Jain
Mathieu Blanchette
Yoshua Bengio
Phylogenetics is a branch of computational biology that studies the evolutionary relationships among biological entities. Its long history a… (voir plus)nd numerous applications notwithstanding, inference of phylogenetic trees from sequence data remains challenging: the high complexity of tree space poses a significant obstacle for the current combinatorial and probabilistic techniques. In this paper, we adopt the framework of generative flow networks (GFlowNets) to tackle two core problems in phylogenetics: parsimony-based and Bayesian phylogenetic inference. Because GFlowNets are well-suited for sampling complex combinatorial structures, they are a natural choice for exploring and sampling from the multimodal posterior distribution over tree topologies and evolutionary distances. We demonstrate that our amortized posterior sampler, PhyloGFN, produces diverse and high-quality evolutionary hypotheses on real benchmark datasets. PhyloGFN is competitive with prior works in marginal likelihood estimation and achieves a closer fit to the target distribution than state-of-the-art variational inference methods. Our code is available at https://github.com/zmy1116/phylogfn.
2024-01-15
ICLR.cc/2024/Conference (poster)
doi.org
openreview.net
Poly-View Contrastive Learning
Amitis Shidani
R Devon Hjelm
Jason Ramapuram
Russell Webb
Eeshan Gunesh Dhekane
Dan Busbridge
2024-01-15
ICLR.cc/2024/Conference (poster)
openreview.net
openreview.net
Pre-Training and Fine-Tuning Generative Flow Networks
Ling Pan
Moksh Jain
Kanika Madan
Yoshua Bengio
Generative Flow Networks (GFlowNets) are amortized samplers that learn stochastic policies to sequentially generate compositional objects fr… (voir plus)om a given unnormalized reward distribution. They can generate diverse sets of high-reward objects, which is an important consideration in scientific discovery tasks. However, as they are typically trained from a given extrinsic reward function, it remains an important open challenge about how to leverage the power of pre-training and train GFlowNets in an unsupervised fashion for efficient adaptation to downstream tasks. Inspired by recent successes of unsupervised pre-training in various domains, we introduce a novel approach for reward-free pre-training of GFlowNets. By framing the training as a self-supervised problem, we propose an outcome-conditioned GFlowNet (OC-GFN) that learns to explore the candidate space. Specifically, OC-GFN learns to reach any targeted outcomes, akin to goal-conditioned policies in reinforcement learning. We show that the pre-trained OC-GFN model can allow for a direct extraction of a policy capable of sampling from any new reward functions in downstream tasks. Nonetheless, adapting OC-GFN on a downstream task-specific reward involves an intractable marginalization over possible outcomes. We propose a novel way to approximate this marginalization by learning an amortized predictor enabling efficient fine-tuning. Extensive experimental results validate the efficacy of our approach, demonstrating the effectiveness of pre-training the OC-GFN, and its ability to swiftly adapt to downstream tasks and discover modes more efficiently. This work may serve as a foundation for further exploration of pre-training strategies in the context of GFlowNets.
2024-01-15
ICLR.cc/2024/Conference (spotlight)
doi.org
openreview.net
Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo
Haque Ishfaq
Qingfeng Lan
Pan Xu
A. Rupam Mahmood
Doina Precup
Anima Anandkumar
Kamyar Azizzadenesheli
We present a scalable and effective exploration strategy based on Thompson sampling for reinforcement learning (RL). One of the key shortcom… (voir plus)ings of existing Thompson sampling algorithms is the need to perform a Gaussian approximation of the posterior distribution, which is not a good surrogate in most practical settings. We instead directly sample the Q function from its posterior distribution, by using Langevin Monte Carlo, an efficient type of Markov Chain Monte Carlo (MCMC) method. Our method only needs to perform noisy gradient descent updates to learn the exact posterior distribution of the Q function, which makes our approach easy to deploy in deep RL. We provide a rigorous theoretical analysis for the proposed method and demonstrate that, in the linear Markov decision process (linear MDP) setting, it has a regret bound of
2024-01-15
ICLR.cc/2024/Conference (poster)
doi.org
openreview.net
Raidar: geneRative AI Detection viA Rewriting
Chengzhi Mao
Carl Vondrick
Hao Wang
Junfeng Yang
We find that large language models (LLMs) are more likely to modify human-written text than AI-generated text when tasked with rewriting. Th… (voir plus)is tendency arises because LLMs often perceive AI-generated text as high-quality, leading to fewer modifications. We introduce a method to detect AI-generated content by prompting LLMs to rewrite text and calculating the editing distance of the output. We dubbed our geneRative AI Detection viA Rewriting method Raidar. Raidar significantly improves the F1 detection scores of existing AI content detection models -- both academic and commercial -- across various domains, including News, creative writing, student essays, code, Yelp reviews, and arXiv papers, with gains of up to 29 points. Operating solely on word symbols without high-dimensional features, our method is compatible with black box LLMs, and is inherently robust on new content. Our results illustrate the unique imprint of machine-generated text through the lens of the machines themselves.
2024-01-15
ICLR.cc/2024/Conference (poster)
doi.org
openreview.net
Reasoning with Latent Diffusion in Offline Reinforcement Learning
Siddarth Venkatraman
Shivesh Khaitan
Ravi Tej Akella
John Dolan
Jeff Schneider
Glen Berseth
Offline reinforcement learning (RL) holds promise as a means to learn high-reward policies from a static dataset, without the need for furth… (voir plus)er environment interactions. However, a key challenge in offline RL lies in effectively stitching portions of suboptimal trajectories from the static dataset while avoiding extrapolation errors arising due to a lack of support in the dataset. Existing approaches use conservative methods that are tricky to tune and struggle with multi-modal data (as we show) or rely on noisy Monte Carlo return-to-go samples for reward conditioning. In this work, we propose a novel approach that leverages the expressiveness of latent diffusion to model in-support trajectory sequences as compressed latent skills. This facilitates learning a Q-function while avoiding extrapolation error via batch-constraining. The latent space is also expressive and gracefully copes with multi-modal data. We show that the learned temporally-abstract latent space encodes richer task-specific information for offline RL tasks as compared to raw state-actions. This improves credit assignment and facilitates faster reward propagation during Q-learning. Our method demonstrates state-of-the-art performance on the D4RL benchmarks, particularly excelling in long-horizon, sparse-reward tasks.
2024-01-15
ICLR.cc/2024/Conference (poster)
doi.org
openreview.net
Reward Model Ensembles Help Mitigate Overoptimization
Thomas Coste
Usman Anwar
Robert Kirk
David M. Krueger
Reinforcement learning from human feedback (RLHF) is a standard approach for fine-tuning large language models to follow instructions. As pa… (voir plus)rt of this process, learned reward models are used to approximately model human preferences. However, as imperfect representations of the “true” reward, these learned reward models are susceptible to overoptimization. Gao et al. (2023) studied this phenomenon in a synthetic human feedback setup with a significantly larger “gold” reward model acting as the true reward (instead of humans) and showed that overoptimization remains a persistent problem regardless of the size of the proxy reward model and training data used. Using a similar setup, we conduct a systematic study to evaluate the efficacy of using ensemble-based conservative optimization objectives, specifically worst-case optimization (WCO) and uncertainty-weighted optimization (UWO), for mitigating reward model overoptimization when using two optimization methods: (a) best-of-n sampling (BoN) (b) proximal policy optimization (PPO). We additionally extend the setup of Gao et al. (2023) to include 25% label noise to better mirror real-world conditions. Both with and without label noise we find that conservative optimization practically eliminates overoptimization and improves performance by up to 70% for BoN sampling. For PPO, ensemble-based conservative optimization always reduces overoptimization and outperforms single reward model optimization. Moreover, combining it with a small KL penalty successfully prevents overoptimization at no performance cost. Overall, our results demonstrate that ensemble-based conservative optimization can effectively counter overoptimization.
2024-01-15
ICLR.cc/2024/Conference (poster)
openreview.net
openreview.net
Searching for High-Value Molecules Using Reinforcement Learning and Transformers
Raj Ghugare
Santiago Miret
Adriana Hugessen
Mariano Phielipp
Glen Berseth
2024-01-15
ICLR.cc/2024/Conference (poster)
doi.org
openreview.net
Str2str: A Score-Based Framework for Zero-Shot Protein Conformation Sampling
Jiarui Lu
Bozitao Zhong
Zuobai Zhang
Jian Tang
The dynamic nature of proteins is crucial for determining their biological functions and properties, for which Monte Carlo (MC) and molecula… (voir plus)r dynamics (MD) simulations stand as predominant tools to study such phenomena. By utilizing empirically derived force fields, MC or MD simulations explore the conformational space through numerically evolving the system via Markov chain or Newtonian mechanics. However, the high-energy barrier of the force fields can hamper the exploration of both methods by the rare event, resulting in inadequately sampled ensemble without exhaustive running. Existing learning-based approaches perform direct sampling yet heavily rely on target-specific simulation data for training, which suffers from high data acquisition cost and poor generalizability. Inspired by simulated annealing, we propose Str2Str, a novel structure-to-structure translation framework capable of zero-shot conformation sampling with roto-translation equivariant property. Our method leverages an amortized denoising score matching objective trained on general crystal structures and has no reliance on simulation data during both training and inference. Experimental results across several benchmarking protein systems demonstrate that Str2Str outperforms previous state-of-the-art generative structure prediction models and can be orders of magnitude faster compared to long MD simulations. Our open-source implementation is available at https://github.com/lujiarui/Str2Str
2024-01-15
ICLR.cc/2024/Conference (poster)
doi.org
openreview.net
Sufficient Conditions for Offline Reactivation in Recurrent Neural Networks
Nanda H Krishna
Colin Bredenberg
Daniel Levenstein
Blake Aaron Richards
Guillaume Lajoie
During periods of quiescence, such as sleep, neural activity in many brain circuits resembles that observed during periods of task engagemen… (voir plus)t. However, the precise conditions under which task-optimized networks can autonomously reactivate the same network states responsible for online behavior is poorly understood. In this study, we develop a mathematical framework that outlines sufficient conditions for the emergence of neural reactivation in circuits that encode features of smoothly varying stimuli. We demonstrate mathematically that noisy recurrent networks optimized to track environmental state variables using change-based sensory information naturally develop denoising dynamics, which, in the absence of input, cause the network to revisit state configurations observed during periods of online activity. We validate our findings using numerical experiments on two canonical neuroscience tasks: spatial position estimation based on self-motion cues, and head direction estimation based on angular velocity cues. Overall, our work provides theoretical support for modeling offline reactivation as an emergent consequence of task optimization in noisy neural circuits.
2024-01-15
International Conference on Learning Representations (poster)
doi.org
openreview.net