Publications

Rethinking Prompt Optimization: Reinforcement, Diversification, and Migration in Blackbox LLMs

MohammadReza Davari

Utkarsh Garg

Weixin Cai

Eugene Belilovsky

2025-06-30

arXiv (publié)

Self-Predictive Representations for Combinatorial Generalization in Behavioral Cloning

Adriana Hugessen

Behavioral cloning (BC) methods trained with supervised learning (SL) are an effective way to learn policies from human demonstrations in do… (voir plus)mains like robotics. Goal-conditioning these policies enables a single generalist policy to capture diverse behaviors contained within an offline dataset. While goal-conditioned behavior cloning (GCBC) methods can perform well on in-distribution training tasks, they do not necessarily generalize zero-shot to tasks that require conditioning on novel state-goal pairs, i.e. combinatorial generalization. In part, this limitation can be attributed to a lack of temporal consistency in the state representation learned by BC; if temporally related states are encoded to similar latent representations, then the out-of-distribution gap for novel state-goal pairs would be reduced. Hence, encouraging this temporal consistency in the representation space should facilitate combinatorial generalization. Successor representations, which encode the distribution of future states visited from the current state, nicely encapsulate this property. However, previous methods for learning successor representations have relied on contrastive samples, temporal-difference (TD) learning, or both. In this work, we propose a simple yet effective representation learning objective,

2025-06-30

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

Speciation of coral-associated barnacles: generalists versus specialists in the Indo-West Pacific

Hsiu-Chin Lin

Lorenzo C. Halasan

Yoko Nozawa

Benny Kwok Kan Chan

2025-06-30

Coral reefs (publié)

On the Fundamental Limitations of Dual Static CVaR Decompositions in Markov Decision Processes

Mathieu Godbout

Audrey Durand

2025-06-30

arXiv (publié)

Using machine learning algorithms to predict students' general self-efficacy in PISA 2018

Bin Tan

Hao-Yue Jin

Maria Cutumisu

2025-06-30

Journal of Applied Developmental Psychology (publié)

What Can Grokking Teach Us About Learning Under Nonstationarity?

Clare Lyle

Gharda Sokar

Razvan Pascanu

Andr'as Gyorgy

In continual learning problems, it is often necessary to overwrite components of a neural network's learned representation in response to ch… (voir plus)anges in the data stream; however, neural networks often exhibit \primacy bias, whereby early training data hinders the network's ability to generalize on later tasks. While feature-learning dynamics of nonstationary learning problems are not well studied, the emergence of feature-learning dynamics is known to drive the phenomenon of grokking, wherein neural networks initially memorize their training data and only later exhibit perfect generalization. This work conjectures that the same feature-learning dynamics which facilitate generalization in grokking also underlie the ability to overwrite previous learned features as well, and methods which accelerate grokking by facilitating feature-learning dynamics are promising candidates for addressing primacy bias in non-stationary learning problems. We then propose a straightforward method to induce feature-learning dynamics as needed throughout training by increasing the effective learning rate, i.e. the ratio between parameter and update norms. We show that this approach both facilitates feature-learning and improves generalization in a variety of settings, including grokking, warm-starting neural network training, and reinforcement learning tasks.

2025-06-30

arXiv (publié)

What Matters for Maximizing Data Reuse In Value-based Deep Reinforcement Learning

Roger Creus Castanyer

Glen Berseth

Pablo Samuel Castro

A key ingredient for successfully applying deep reinforcement learning to challenging tasks is the effective use of data at scale. Although … (voir plus)originally deep RL algorithms achieved this by storing past experiences collected from a synchronous actor in an external replay memory [DQN; Mnih et al., 2013], follow-up works scaled training by collecting data asynchronously through distributed actors [R2D2; Kapturowski et al., 2018], and more recently by GPU-optimized parallelization [PQN; Gallici et al., 2024]. We argue that DQN, PQN, and R2D2 constitute a group of value-based methods for parallel training and study them to shed light on the dynamics induced by varying data collection schemes. We conduct a thorough empirical study to better understand these dynamics, and propose the Data Replay Ratio as a novel metric for quantifying data reuse. Our findings suggest that maximizing data reuse involves directly addressing the deadly triad: Q-lambda rollouts for reducing the bias from bootstrapping, the use of LayerNorm for stabilizing function approximation, and parallelized data collection for mitigating off-policy divergence.

2025-06-30

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

WiSE-OD: Benchmarking Robustness in Infrared Object Detection

Heitor Rapela Medeiros

Atif Belal

Masih Aminbeidokhti

Eric Granger

Marco Pedersoli

2025-06-30

arXiv (publié)

Zero-Shot Constraint Satisfaction with Forward- Backward Representations

Adriana Hugessen

Harley Wiltzer

Cyrus Neary

Amy Zhang

Glen Berseth

Traditionally, constrained policy optimization with Reinforcement Learning (RL) requires learning a new policy from scratch for any new envi… (voir plus)ronment, goal or cost function, with limited generalization to new tasks and constraints. Given the sample inefficiency of many common deep RL methods, this procedure can be impractical for many real-world scenarios, particularly when constraints or tasks are changing. As an alternative, in the unconstrained setting, various works have sought to pre-train representations from offline datasets to accelerate policy optimization upon specification of a reward. Such methods can permit faster adaptation to new tasks in a given environment, dramatically improving sample efficiency. Recently, zero-shot policy optimization has been explored by leveraging a particular

2025-06-30

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

Circuit Discovery Helps To Detect LLM Jailbreaking

Despite extensive safety alignment, large language models (LLMs) remain vulnerable to jailbreak attacks that bypass safeguards to elicit har… (voir plus)mful content. While prior work attributes this vulnerability to safety training limitations, the internal mechanisms by which LLMs process adversarial prompts remain poorly understood. We present a mechanistic analysis of the jailbreaking behavior in a large-scale, safety-aligned LLM, focusing on LLaMA-2-7B-chat-hf. Leveraging edge attribution patching and subnetwork probing, we systematically identify computational circuits responsible for generating affirmative responses to jailbreak prompts. Ablating these circuits during the first token prediction can reduce attack success rates by up to 80\%, demonstrating its critical role in safety bypass. Our analysis uncovers key attention heads and MLP pathways that mediate adversarial prompt exploitation, revealing how important tokens propagate through these components to override safety constraints. These findings advance the understanding of adversarial vulnerabilities in aligned LLMs and pave the way for targeted, interpretable defenses mechanisms based on mechanistic interpretability.

2025-06-29

ICML.cc/2025/Workshop/R2-FM (poster)