Preservice Teachers’ Computational Thinking Profiles
Tanya Chichekian
Maria Cutumisu
Annie Savard
2025-06-10
Proceedings (International Conference of the Learning Sciences) (published)
doi.org
Robust Reward Modeling via Causal Rubrics
Pragya Srivastava
Harman Singh
Rahul Madhavan
Gandharv Patil
Sravanti Addepalli
Arun Suggala
Rengarajan Aravamudhan
Soumya Sharma
Anirban Laha
Aravindan Raghuveer
Karthikeyan Shanmugam
Doina Precup
Reward models (RMs) for LLM alignment often exhibit reward hacking, mistaking spurious correlates (e.g., length, format) for causal quality … (see more)drivers (e.g., factuality, relevance), leading to brittle RMs. We introduce CROME (Causally Robust Reward Modeling), a causally-grounded framework using targeted augmentations to mitigate this. CROME employs: (1) Causal Augmentations, pairs isolating specific causal attribute changes, to enforce sensitivity, and (2) Neutral Augmentations, tie-labeled pairs varying spurious attributes while preserving causal content, to enforce invariance. Crucially, augmentations target LLM-identified causal rubrics, requiring no prior knowledge of spurious factors. CROME significantly outperforms baselines on RewardBench (Avg +5.4\%, Safety +13.2\%, Reasoning +7.2\%) and demonstrates enhanced robustness via improved Best-of-N performance across RewardBench, WildGuardTest, and GSM8k.
2025-06-10
ICML.cc/2025/Workshop/MoFA (poster)
doi.org
openreview.net
A Systematic Literature Review of Large Language Model Applications in the Algebra Domain
Yajie Song
Yimei Zhang
Doina Precup
Reihaneh Rabbany
Maria Cutumisu
2025-06-10
Proceedings (International Conference of the Learning Sciences) (published)
doi.org
Test Time Adaptation Using Adaptive Quantile Recalibration
Paria Mehrbod
Pedro Vianna
geraldin nanfack
Guy Wolf
Eugene Belilovsky
2025-06-10
ICML.cc/2025/Workshop/PUT (poster)
openreview.net
openreview.net
What Matters when Modeling Human Behavior using Imitation Learning?
Aneri Muni
Esther Derman
Vincent Taboga
Pierre-Luc Bacon
Erick Delage
As AI systems become increasingly embedded in human decision-making process, aligning their behavior with human values is critical to ensuri… (see more)ng safe and trustworthy deployment. A central approach to AI Alignment called Imitation Learning (IL), trains a learner to directly mimic desirable human behaviors from expert demonstrations. However, standard IL methods assume that (1) experts act to optimize expected returns; (2) expert policies are Markovian. Both assumptions are inconsistent with empirical findings from behavioral economics, according to which humans are (1) risk-sensitive; and (2) make decisions based on past experience. In this work, we examine the implications of risk sensitivity for IL and show that standard approaches do not capture all optimal policies under risk-sensitive decision criteria. By characterizing these expert policies, we identify key limitations of existing IL algorithms in replicating expert performance in risk-sensitive settings. Our findings underscore the need for new IL frameworks that account for both risk-aware preferences and temporal dependencies to faithfully align AI behavior with human experts.
2025-06-10
ICML.cc/2025/Workshop/MoFA (poster)
openreview.net
openreview.net
Adversarial Attack Classification and Robustness Testing for Large Language Models for Code
Yang Liu
Armstrong Foundjem
Foutse Khomh
Heng Li
Large Language Models (LLMs) have become vital tools in software development tasks such as code generation, completion, and analysis. As the… (see more)ir integration into workflows deepens, ensuring robustness against vulnerabilities especially those triggered by diverse or adversarial inputs becomes increasingly important. Such vulnerabilities may lead to incorrect or insecure code generation when models encounter perturbed task descriptions, code, or comments. Prior research often overlooks the role of natural language in guiding code tasks. This study investigates how adversarial perturbations in natural language inputs including prompts, comments, and descriptions affect LLMs for Code (LLM4Code). It examines the effects of perturbations at the character, word, and sentence levels to identify the most impactful vulnerabilities. We analyzed multiple projects (e.g., ReCode, OpenAttack) and datasets (e.g., HumanEval, MBPP), establishing a taxonomy of adversarial attacks. The first dimension classifies the input type code, prompts, or comments while the second dimension focuses on granularity: character, word, or sentence-level changes. We adopted a mixed-methods approach, combining quantitative performance metrics with qualitative vulnerability analysis. LLM4Code models show varying robustness across perturbation types. Sentence-level attacks were least effective, suggesting models are resilient to broader contextual changes. In contrast, word-level perturbations posed serious challenges, exposing semantic vulnerabilities. Character-level effects varied, showing model sensitivity to subtle syntactic deviations.Our study offers a structured framework for testing LLM4Code robustness and emphasizes the critical role of natural language in adversarial evaluation. Improving model resilience to semantic-level disruptions is essential for secure and reliable code-generation systems.
2025-06-09
ArXiv (preprint)
arxiv.org
arxiv.org
Improving Context Fidelity via Native Retrieval-Augmented Reasoning
Suyuchen Wang
Jinlin Wang
Xinyu Wang
Shiqi Li
Xiangru Tang
Sirui Hong
Xiao-Wen Chang
Chenglin Wu
Bang Liu
Large language models (LLMs) often struggle with context fidelity, producing inconsistent answers when responding to questions based on prov… (see more)ided information. Existing approaches either rely on expensive supervised fine-tuning to generate evidence post-answer or train models to perform web searches without necessarily improving utilization of the given context. We propose CARE, a novel native retrieval-augmented reasoning framework that teaches LLMs to explicitly integrate in-context evidence within their reasoning process with the model's own retrieval capabilities. Our method requires minimal labeled evidence data while significantly enhancing both retrieval accuracy and answer generation performance through strategically retrieved in-context tokens in the reasoning chain. Extensive experiments on multiple real-world and counterfactual QA benchmarks demonstrate that our approach substantially outperforms supervised fine-tuning, traditional retrieval-augmented generation methods, and external retrieval solutions. This work represents a fundamental advancement in making LLMs more accurate, reliable, and efficient for knowledge-intensive tasks.
2025-06-09
ICML.cc/2025/Workshop/LCFM (accepted)
openreview.net
openreview.net
PyLO: Towards Accessible Learned Optimizers in PyTorch
Paul Janson
Benjamin Thérien
Quentin Gregory Anthony
Xiaolong Huang
Abhinav Moudgil
Eugene Belilovsky
Learned optimizers have been an active research topic over the past decade, with increasing progress toward practical, general-purpose optim… (see more)izers that can serve as drop-in replacements for widely used methods like Adam. However, recent advances -- such as VeLO, which was meta-trained for 4000 TPU-months -- remain largely inaccessible to the broader community, in part due to their reliance on JAX and the absence of user-friendly packages for applying the optimizers after meta-training. To address this gap, we introduce PyLO, a PyTorch-based library that brings learned optimizers to the broader machine learning community through familiar, widely adopted workflows. Unlike prior work focused on synthetic or convex tasks, our emphasis is on applying learned optimization to real-world large-scale pre-training tasks. Our release includes a CUDA-accelerated version of the small_fc_lopt learned optimizer architecture from (Metz et al., 2022a), delivering substantial speedups -- from 39.36 to 205.59 samples/sec throughput for training ViT B/16 with batch size 32. PyLO also allows us to easily combine learned optimizers with existing optimization tools such as learning rate schedules and weight decay. When doing so, we find that learned optimizers can substantially benefit. Our code is available at https://github.com/Belilovsky-Lab/pylo
2025-06-09
ICML.cc/2025/Workshop/CODEML (published)
doi.org
openreview.net
Revisiting the Goldilocks Zone in Inhomogeneous Networks
Zacharie Garnier Cuchet
Sarath Chandar
Ekaterina Lobacheva
We investigate how architectural inhomogeneities—such as biases, layer normalization, and residual connections—affect the curvature of t… (see more)he loss landscape at initialization and its link to trainability. We focus on the Goldilocks zone, a region in parameter space with excess positive curvature, previously associated with improved optimization in homogeneous networks. To extend this analysis, we compare two scaling strategies: weight scaling and softmax temperature scaling. Our results show that in networks with biases or residual connections, both strategies identify a Goldilocks zone aligned with better training. In contrast, layer normalization leads to lower or negative curvature, yet stable optimization—revealing a disconnect between curvature and trainability. Softmax temperature scaling behaves more consistently across models, making it a more robust probe. Overall, the Goldilocks zone remains relevant in inhomogeneous networks, but its geometry and predictive power depend on architectural choices, particularly normalization.
2025-06-09
ICML.cc/2025/Workshop/HiLD (poster)
openreview.net
openreview.net
Spaced Scheduling for Large Language Model Training
Amine El hattami
Nicolas Chapados
Chris Pal
2025-06-09
TMLR (accepted)
openreview.net
openreview.net
Towards Fair In-Context Learning with Tabular Foundation Models
Patrik Joslin Kenfack
Samira Ebrahimi Kahou
Ulrich Aivodji
Tabular foundational models have shown promising in-context learning capabilities on structured data by using training examples as context w… (see more)ithout further parameter adjustments. This emerging approach positions itself as a competitive alternative to traditional gradient-boosted tree methods. However, while biases in conventional machine learning models are well documented, it remains unclear how these biases manifest in Tabular ICL. The paper investigates the fairness implications of Tabular ICL and explores three preprocessing strategies—correlation removal, group-balanced demonstration selection, and uncertainty-based demonstration selection—to address bias. Comprehensive experiments indicate that uncertainty-based demonstration selection consistently enhances group fairness in the predictions. The source code for reproducing the results of this work can be found at https://anonymous.4open.science/r/Fair-TabICL-DD84.
2025-06-09
ICML.cc/2025/Workshop/FMSD (published)
openreview.net
openreview.net
Towards Fair In-Context Learning with Tabular Foundation Models
Patrik Joslin Kenfack
Samira Ebrahimi Kahou
Ulrich Aivodji
Tabular foundational models have shown promising in-context learning capabilities on structured data by using training examples as context w… (see more)ithout further parameter adjustments. This emerging approach positions itself as a competitive alternative to traditional gradient-boosted tree methods. However, while biases in conventional machine learning models are well documented, it remains unclear how these biases manifest in Tabular ICL. The paper investigates the fairness implications of Tabular ICL and explores three preprocessing strategies—correlation removal, group-balanced demonstration selection, and uncertainty-based demonstration selection—to address bias. Comprehensive experiments indicate that uncertainty-based demonstration selection consistently enhances group fairness in the predictions. The source code for reproducing the results of this work can be found at https://anonymous.4open.science/r/Fair-TabICL-DD84.
2025-06-09
ICML.cc/2025/Workshop/FMSD (published)
openreview.net
openreview.net