Publications

Discovering Temporal Structure: An Overview of Hierarchical Reinforcement Learning
Martin Klissarov
Akhil Bagaria
Ziyan Luo
George Konidaris
Doina Precup
Marlos C. Machado
Developing agents capable of exploring, planning and learning in complex open-ended environments is a grand challenge in artificial intellig… (see more)ence (AI). Hierarchical reinforcement learning (HRL) offers a promising solution to this challenge by discovering and exploiting the temporal structure within a stream of experience. The strong appeal of the HRL framework has led to a rich and diverse body of literature attempting to discover a useful structure. However, it is still not clear how one might define what constitutes good structure in the first place, or the kind of problems in which identifying it may be helpful. This work aims to identify the benefits of HRL from the perspective of the fundamental challenges in decision-making, as well as highlight its impact on the performance trade-offs of AI agents. Through these benefits, we then cover the families of methods that discover temporal structure in HRL, ranging from learning directly from online experience to offline datasets, to leveraging large language models (LLMs). Finally, we highlight the challenges of temporal structure discovery and the domains that are particularly well-suited for such endeavours.
2025-06-16
ArXiv (preprint)
doi.org
arxiv.org
Discovering Temporal Structure: An Overview of Hierarchical Reinforcement Learning
Martin Klissarov
Akhil Bagaria
Ziyan Luo
George Konidaris
Doina Precup
Marlos C. Machado
Developing agents capable of exploring, planning and learning in complex open-ended environments is a grand challenge in artificial intellig… (see more)ence (AI). Hierarchical reinforcement learning (HRL) offers a promising solution to this challenge by discovering and exploiting the temporal structure within a stream of experience. The strong appeal of the HRL framework has led to a rich and diverse body of literature attempting to discover a useful structure. However, it is still not clear how one might define what constitutes good structure in the first place, or the kind of problems in which identifying it may be helpful. This work aims to identify the benefits of HRL from the perspective of the fundamental challenges in decision-making, as well as highlight its impact on the performance trade-offs of AI agents. Through these benefits, we then cover the families of methods that discover temporal structure in HRL, ranging from learning directly from online experience to offline datasets, to leveraging large language models (LLMs). Finally, we highlight the challenges of temporal structure discovery and the domains that are particularly well-suited for such endeavours.
2025-06-16
ArXiv (preprint)
doi.org
arxiv.org
Meta-learning how to Share Credit among Macro-Actions
Ionel-Alexandru Hosu
Traian Rebedea
Razvan Pascanu
2025-06-16
ArXiv (preprint)
doi.org
arxiv.org
The Courage to Stop: Overcoming Sunk Cost Fallacy in Deep Reinforcement Learning
Jiashun Liu
Johan Samir Obando Ceron
Pablo Samuel Castro
Aaron Courville
Ling Pan
Off-policy deep reinforcement learning (RL) typically leverages replay buffers for reusing past experiences during learning. This can help i… (see more)mprove sample efficiency when the collected data is informative and aligned with the learning objectives; when that is not the case, it can have the effect of"polluting"the replay buffer with data which can exacerbate optimization challenges in addition to wasting environment interactions due to wasteful sampling. We argue that sampling these uninformative and wasteful transitions can be avoided by addressing the sunk cost fallacy, which, in the context of deep RL, is the tendency towards continuing an episode until termination. To address this, we propose learn to stop (LEAST), a lightweight mechanism that enables strategic early episode termination based on Q-value and gradient statistics, which helps agents recognize when to terminate unproductive episodes early. We demonstrate that our method improves learning efficiency on a variety of RL algorithms, evaluated on both the MuJoCo and DeepMind Control Suite benchmarks.
2025-06-16
ArXiv (preprint)
doi.org
arxiv.org
The Courage to Stop: Overcoming Sunk Cost Fallacy in Deep Reinforcement Learning
Jiashun Liu
Johan Samir Obando Ceron
Pablo Samuel Castro
Aaron Courville
Ling Pan
Off-policy deep reinforcement learning (RL) typically leverages replay buffers for reusing past experiences during learning. This can help i… (see more)mprove sample efficiency when the collected data is informative and aligned with the learning objectives; when that is not the case, it can have the effect of"polluting"the replay buffer with data which can exacerbate optimization challenges in addition to wasting environment interactions due to wasteful sampling. We argue that sampling these uninformative and wasteful transitions can be avoided by addressing the sunk cost fallacy, which, in the context of deep RL, is the tendency towards continuing an episode until termination. To address this, we propose learn to stop (LEAST), a lightweight mechanism that enables strategic early episode termination based on Q-value and gradient statistics, which helps agents recognize when to terminate unproductive episodes early. We demonstrate that our method improves learning efficiency on a variety of RL algorithms, evaluated on both the MuJoCo and DeepMind Control Suite benchmarks.
2025-06-16
ArXiv (preprint)
doi.org
arxiv.org
Can GPT4 Generate Effective Feedback on Code Readability?
Xiaotian Su
Yajie Song
Marcus Messer
Jaromir Savelka
Maria Cutumisu
April Wang
2025-06-13
Annual Conference on Innovation and Technology in Computer Science Education (published)
doi.org
Detecting High-Stakes Interactions with Activation Probes
Alex McKenzie
Urja Pawar
Phil Blandfort
William Bankes
David Scott Krueger
Ekdeep Singh Lubana
Dmitrii Krasheninnikov
Monitoring is an important aspect of safely deploying Large Language Models (LLMs). This paper examines activation probes for detecting"high… (see more)-stakes"interactions -- where the text indicates that the interaction might lead to significant harm -- as a critical, yet underexplored, target for such monitoring. We evaluate several probe architectures trained on synthetic data, and find them to exhibit robust generalization to diverse, out-of-distribution, real-world data. Probes' performance is comparable to that of prompted or finetuned medium-sized LLM monitors, while offering computational savings of six orders-of-magnitude. Our experiments also highlight the potential of building resource-aware hierarchical monitoring systems, where probes serve as an efficient initial filter and flag cases for more expensive downstream analysis. We release our novel synthetic dataset and codebase to encourage further study.
2025-06-12
ArXiv (preprint)
arxiv.org
arxiv.org
Detecting High-Stakes Interactions with Activation Probes
Alex McKenzie
Urja Pawar
Phil Blandfort
William Bankes
David Scott Krueger
Ekdeep Singh Lubana
Dmitrii Krasheninnikov
Monitoring is an important aspect of safely deploying Large Language Models (LLMs). This paper examines activation probes for detecting "hig… (see more)h-stakes" interactions -- where the text indicates that the interaction might lead to significant harm -- as a critical, yet underexplored, target for such monitoring. We evaluate several probe architectures trained on synthetic data, and find them to exhibit robust generalization to diverse, out-of-distribution, real-world data. Probes' performance is comparable to that of prompted or finetuned medium-sized LLM monitors, while offering computational savings of six orders-of-magnitude. Our experiments also highlight the potential of building resource-aware hierarchical monitoring systems, where probes serve as an efficient initial filter and flag cases for more expensive downstream analysis. We release our novel synthetic dataset and codebase to encourage further study.
2025-06-12
ArXiv (preprint)
doi.org
arxiv.org
Detecting High-Stakes Interactions with Activation Probes
Alex McKenzie
Urja Pawar
Phil Blandfort
William Bankes
David Scott Krueger
Ekdeep Singh Lubana
Dmitrii Krasheninnikov
Monitoring is an important aspect of safely deploying Large Language Models (LLMs). This paper examines activation probes for detecting"high… (see more)-stakes"interactions -- where the text indicates that the interaction might lead to significant harm -- as a critical, yet underexplored, target for such monitoring. We evaluate several probe architectures trained on synthetic data, and find them to exhibit robust generalization to diverse, out-of-distribution, real-world data. Probes' performance is comparable to that of prompted or finetuned medium-sized LLM monitors, while offering computational savings of six orders-of-magnitude. Our experiments also highlight the potential of building resource-aware hierarchical monitoring systems, where probes serve as an efficient initial filter and flag cases for more expensive downstream analysis. We release our novel synthetic dataset and codebase to encourage further study.
2025-06-12
ArXiv (preprint)
doi.org
arxiv.org
Discrete Audio Tokens: More Than a Survey!
Pooneh Mousavi
Gallil Maimon
Adel Moumen
Darius Petermann
Jiatong Shi
Haibin Wu
Haici Yang
Anastasia Kuznetsova
Artem Ploujnikov
Ricard Marxer
Bhuvana Ramabhadran
Benjamin Elizalde
Loren Lugosch
Jinyu Li
Cem Subakan
Phil Woodland
Minje Kim
Hung-yi Lee
Shinji Watanabe
Yossi Adi … (see 1 more)
Mirco Ravanelli
Discrete audio tokens are compact representations that aim to preserve perceptual quality, phonetic content, and speaker characteristics whi… (see more)le enabling efficient storage and inference, as well as competitive performance across diverse downstream tasks.They provide a practical alternative to continuous features, enabling the integration of speech and audio into modern large language models (LLMs). As interest in token-based audio processing grows, various tokenization methods have emerged, and several surveys have reviewed the latest progress in the field. However, existing studies often focus on specific domains or tasks and lack a unified comparison across various benchmarks. This paper presents a systematic review and benchmark of discrete audio tokenizers, covering three domains: speech, music, and general audio. We propose a taxonomy of tokenization approaches based on encoder-decoder, quantization techniques, training paradigm, streamability, and application domains. We evaluate tokenizers on multiple benchmarks for reconstruction, downstream performance, and acoustic language modeling, and analyze trade-offs through controlled ablation studies. Our findings highlight key limitations, practical considerations, and open challenges, providing insight and guidance for future research in this rapidly evolving area. For more information, including our main results and tokenizer database, please refer to our website: https://poonehmousavi.github.io/dates-website/.
2025-06-12
ArXiv (preprint)
arxiv.org
arxiv.org
Discrete Audio Tokens: More Than a Survey!
Pooneh Mousavi
Gallil Maimon
Adel Moumen
Darius Petermann
Jiatong Shi
Haibin Wu
Haici Yang
Anastasia Kuznetsova
Artem Ploujnikov
Ricard Marxer
Bhuvana Ramabhadran
Benjamin Elizalde
Loren Lugosch
Jinyu Li
Cem Subakan
Phil Woodland
Minje Kim
Hung-yi Lee
Shinji Watanabe
Yossi Adi … (see 1 more)
Mirco Ravanelli
Discrete audio tokens are compact representations that aim to preserve perceptual quality, phonetic content, and speaker characteristics whi… (see more)le enabling efficient storage and inference, as well as competitive performance across diverse downstream tasks. They provide a practical alternative to continuous features, enabling the integration of speech and audio into modern large language models (LLMs). As interest in token-based audio processing grows, various tokenization methods have emerged, and several surveys have reviewed the latest progress in the field. However, existing studies often focus on specific domains or tasks and lack a unified comparison across various benchmarks. This paper presents a systematic review and benchmark of discrete audio tokenizers, covering three domains: speech, music, and general audio. We propose a taxonomy of tokenization approaches based on encoder-decoder, quantization techniques, training paradigm, streamability, and application domains. We evaluate tokenizers on multiple benchmarks for reconstruction, downstream performance, and acoustic language modeling, and analyze trade-offs through controlled ablation studies. Our findings highlight key limitations, practical considerations, and open challenges, providing insight and guidance for future research in this rapidly evolving area. For more information, including our main results and tokenizer database, please refer to our website: https://poonehmousavi.github.io/dates-website/.
2025-06-12
ArXiv (preprint)
doi.org
arxiv.org
Exploration by Exploitation: Curriculum Learning for Reinforcement Learning Agents through Competence-Based Curriculum Policy Search
Tabitha Edith Lee
Nan Rosemary Ke
Sarvesh Patil
Annya Dahmani
Eunice Yiu
Esra'a Saleh
Alison Gopnik
Oliver Kroemer
Glen Berseth
2025-06-12
ICML.cc/2025/Workshop/EXAIT (poster)
openreview.net
openreview.net