Publications

arxiv.org

Curiosity-Driven Exploration via Temporal Contrastive Learning

Faisal Mohamed

Catherine Ji

Benjamin Eysenbach

Effective exploration in reinforcement learning requires keeping track not just of where the agent has been, but also of how the agent think… (see more)s about and represents the world: an agent should explore states that enable it to learn powerful representations. Temporal representations can include the information required to solve any potential task while avoiding the computational cost of reconstruction. In this paper, we propose an exploration method that uses temporal contrastive representations to drive exploration, maximizing coverage as seen through the lens of these temporal representations. We demonstrate complex exploration behaviors in locomotion, manipulation, and embodied-AI tasks, revealing previously unknown capabilities and behaviors once achievable only via extrinsic rewards.

2025-07-01

rl-conference.cc/RLC/2025/Workshop/RLBrew (published)

Curiosity-Driven Exploration via Temporal \\ Contrastive Learning

Faisal Mohamed

Catherine Ji

Benjamin Eysenbach

Exploration remains a key challenge in reinforcement learning (RL), especially in long-horizon tasks and environments with high-dimensional … (see more)observations. A common strategy for effective exploration is to promote state coverage or novelty, which often involves estimating the agent's state visitation distribution. In this paper, we propose \textbf{C}uriosity-Driven Exploration via \textbf{Te}mporal \textbf{C}ontrastive Learning (\methodName), an exploration method based on temporal contrastive learning that rewards agents for reaching states with unexpected futures. This incentivizes uncovering meaningful less-visited states. \methodName is simple and does not require explicit density or uncertainty estimation, while learning representations aligned with the RL objective. It consistently outperforms standard baselines in complex mazes using different embodiments (Ant and Humanoid) and robotic manipulation tasks, while also yielding more diverse behaviors in Craftax without requiring task-specific information.

2025-07-01

rl-conference.cc/RLC/2025/Workshop/RLBrew (published)

Is Exploration or Optimization the Problem for Deep Reinforcement Learning?

In the era of deep reinforcement learning, making progress is more complex, as the collected experience must be compressed into a deep model… (see more) for future exploitation and sampling. Many papers have shown that training a deep learning policy under the changing state and action distribution leads to sub-optimal performance even collapse. This naturally leads to the concern that even if the community creates improved exploration algorithms or reward objectives, will those improvements fall on the \textit{deaf ears} of optimization difficulties. This work proposes a new \textit{pracitcal} sub-optimality estimator to determine optimization limitations of deep reinforcement learning algorithms. Through experiments acrossenvironments and RL algorithms, it is shown that the difference between the best data generated is

2025-07-01

rl-conference.cc/RLC/2025/Workshop/Finding_the_Frame (published)

Is Exploration or Optimization the Problem for Deep Reinforcement Learning?

2025-07-01

rl-conference.cc/RLC/2025/Workshop/Finding_the_Frame (published)

Filter Equivariant Functions: A symmetric account of length-general extrapolation on lists

Owen Lewis

Neil Ghani

Andrew Joseph Dudzik

Christos Perivolaropoulos

Razvan Pascanu

Petar Veličković

2025-07-01

arXiv (published)

From Black Box to Biomarker: Sparse Autoencoders for Interpreting Speech Models of Parkinson's Disease

Peter Plantinga

Jen-Kai Chen

Roozbeh Sattari

Mirco Ravanelli

Denise Klein

Speech holds promise as a cost-effective and non-invasive biomarker for neurological conditions such as Parkinson's disease (PD). While deep… (see more) learning systems trained on raw audio can find subtle signals not available from hand-crafted features, their black-box nature hinders clinical adoption. To address this, we apply sparse autoencoders (SAEs) to uncover interpretable internal representations from a speech-based PD detection system. We introduce a novel mask-based activation for adapting SAEs to small biomedical datasets, creating sparse disentangled dictionary representations. These dictionary entries are found to have strong associations with characteristic articulatory deficits in PD speech, such as reduced spectral flux and increased spectral flatness in the low-energy regions highlighted by the model attention. We further show that the spectral flux is related to volumetric measurements of the putamen from MRI scans, demonstrating the potential of SAEs to reveal clinically relevant biomarkers for disease monitoring and diagnosis.

2025-07-01

arXiv (published)

arxiv.org

A Geometric Lens on RL Environment Complexity Based on Ricci Curvature

Ali Saheb Pasand

Pablo Samuel Castro

Pouya Bashivan

We introduce Ollivier-Ricci Curvature (ORC) as an information-geometric tool for analyzing the local structure of reinforcement learning (RL… (see more)) environments. We establish a novel connection between ORC and the Successor Representation (SR), enabling a geometric interpretation of environment dynamics decoupled from reward signals. Our analysis shows that states with positive and negative ORC values correspond to regions where random walks converge and diverge respectively, which are often critical for effective exploration. ORC is highly correlated with established environment complexity metrics, yet integrates naturally with standard RL frameworks based on SR and provides both global and local complexity measures. Leveraging this property, we propose an ORC-based intrinsic reward that guides agents toward divergent regions and away from convergent traps. Empirical results demonstrate that our curvature-driven reward substantially improves exploration performance across diverse environments, outperforming both random and count-based intrinsic baselines.

2025-07-01

rl-conference.cc/RLC/2025/Workshop/Finding_the_Frame (published)

A Geometric Lens on RL Environment Complexity Based on Ricci Curvature

Ali Saheb Pasand

Pablo Samuel Castro

Pouya Bashivan

We introduce Ollivier-Ricci Curvature (ORC) as an information-geometric tool for analyzing the local structure of reinforcement learning (RL… (see more)) environments. We establish a novel connection between ORC and the Successor Representation (SR), enabling a geometric interpretation of environment dynamics decoupled from reward signals. Our analysis shows that states with positive and negative ORC values correspond to regions where random walks converge and diverge respectively, which are often critical for effective exploration. ORC is highly correlated with established environment complexity metrics, yet integrates naturally with standard RL frameworks based on SR and provides both global and local complexity measures. Leveraging this property, we propose an ORC-based intrinsic reward that guides agents toward divergent regions and away from convergent traps. Empirical results demonstrate that our curvature-driven reward substantially improves exploration performance across diverse environments, outperforming both random and count-based intrinsic reward baselines.

2025-07-01

rl-conference.cc/RLC/2025/Workshop/RLBrew (published)

A Geometric Lens on RL Environment Complexity Based on Ricci Curvature

Ali Saheb Pasand

Pablo Samuel Castro

Pouya Bashivan

We introduce Ollivier-Ricci Curvature (ORC) as an information-geometric tool for analyzing the local structure of reinforcement learning (RL… (see more)) environments. We establish a novel connection between ORC and the Successor Representation (SR), enabling a geometric interpretation of environment dynamics decoupled from reward signals. Our analysis shows that states with positive and negative ORC values correspond to regions where random walks converge and diverge respectively, which are often critical for effective exploration. ORC is highly correlated with established environment complexity metrics, yet integrates naturally with standard RL frameworks based on SR and provides both global and local complexity measures. Leveraging this property, we propose an ORC-based intrinsic reward that guides agents toward divergent regions and away from convergent traps. Empirical results demonstrate that our curvature-driven reward substantially improves exploration performance across diverse environments, outperforming both random and count-based intrinsic baselines.

2025-07-01

rl-conference.cc/RLC/2025/Workshop/Finding_the_Frame (published)

GitChameleon 2.0: Evaluating AI Code Generation Against Python Library Version Incompatibilities

Victor May

Eilif Benjamin Muller

Irina Rish

Samira Ebrahimi Kahou

Massimo Caccia

The rapid evolution of software libraries poses a considerable hurdle for code generation, necessitating continuous adaptation to frequent v… (see more)ersion updates while preserving backward compatibility. While existing code evolution benchmarks provide valuable insights, they typically lack execution-based evaluation for generating code compliant with specific library versions. To address this, we introduce GitChameleon 2.0, a novel, meticulously curated dataset comprising 328 Python code completion problems, each conditioned on specific library versions and accompanied by executable unit tests. GitChameleon 2.0 rigorously evaluates the capacity of contemporary large language models (LLMs), LLM-powered agents, code assistants, and RAG systems to perform version-conditioned code generation that demonstrates functional accuracy through execution. Our extensive evaluations indicate that state-of-the-art systems encounter significant challenges with this task; enterprise models achieving baseline success rates in the 48-51% range, underscoring the intricacy of the problem. By offering an execution-based benchmark emphasizing the dynamic nature of code libraries, GitChameleon 2.0 enables a clearer understanding of this challenge and helps guide the development of more adaptable and dependable AI code generation methods. We make the dataset and evaluation code publicly available at https://github.com/mrcabbage972/GitChameleonBenchmark.

2025-07-01

arXiv (published)

Hallucination Detox: Sensitivity Dropout (SenD) for Large Language Model Training

Shahrad Mohammadzadeh

Juan David Guerra

Marco Bonizzato

Reihaneh Rabbany

Golnoosh Farnadi

2025-07-01

Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (published)