Author name cluster

Jun Shern Chan

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5 papers

2 author rows

ICLR Conference 2025 Conference Paper

MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering

Jun Shern Chan
Neil Chowdhury
Oliver Jaffe
James Aung
Dane Sherburn
Evan Mays
Giulio Starace
Kevin Liu

We introduce MLE-bench, a benchmark for measuring how well AI agents perform at machine learning engineering. To this end, we curate 75 ML engineering-related competitions from Kaggle, creating a diverse set of challenging tasks that test real-world ML engineering skills such as training models, preparing datasets, and running experiments. We establish human baselines for each competition using Kaggle's publicly available leaderboards. We use open-source agent scaffolds to evaluate several frontier language models on our benchmark, finding that the best-performing setup — OpenAI's o1-preview with AIDE scaffolding — achieves at least the level of a Kaggle bronze medal in 16.9% of competitions. In addition to our main results, we investigate various forms of resource-scaling for AI agents and the impact of contamination from pre-training. We open-source our benchmark code https://github.com/openai/mle-bench to facilitate future research in understanding the ML engineering capabilities of AI agents.

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ICML Conference 2025 Conference Paper

PaperBench: Evaluating AI's Ability to Replicate AI Research

Giulio Starace
Oliver Jaffe
Dane Sherburn
James Aung
Jun Shern Chan
Leon Maksin
Rachel Dias
Evan Mays

We introduce PaperBench, a benchmark evaluating the ability of AI agents to replicate state-of-the-art AI research. Agents must replicate 20 ICML 2024 Spotlight and Oral papers from scratch, including understanding paper contributions, developing a codebase, and successfully executing experiments. For objective evaluation, we develop rubrics that hierarchically decompose each replication task into smaller sub-tasks with clear grading criteria. In total, PaperBench contains 8, 316 individually gradable tasks. Rubrics are co-developed with the author(s) of each ICML paper for accuracy and realism. To enable scalable evaluation, we also develop an LLM-based judge to automatically grade replication attempts against rubrics, and assess our judge’s performance by creating a separate benchmark for judges. We evaluate several frontier models on PaperBench, finding that the best-performing tested agent, Claude 3. 5 Sonnet (New) with open-source scaffolding, achieves an average replication score of 21. 0%. Finally, we recruit top ML PhDs to attempt a subset of PaperBench, finding that models do not yet outperform the human baseline. We open-source our code (https: //github. com/openai/preparedness) to facilitate future research in understanding the AI engineering capabilities of AI agents.

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TMLR Journal 2024 Journal Article

Learning from Natural Language Feedback

Angelica Chen
Jérémy Scheurer
Jon Ander Campos
Tomasz Korbak
Jun Shern Chan
Samuel R. Bowman
Kyunghyun Cho
Ethan Perez

The potential for pre-trained large language models (LLMs) to use natural language feedback at inference time has been an exciting recent development. We build upon this observation by formalizing an algorithm for learning from natural language feedback at training time instead, which we call Imitation learning from Language Feedback (ILF). ILF requires only a small amount of human-written feedback during training and does not require the same feedback at test time, making it both user-friendly and sample-efficient. We further show that ILF can be seen as a form of minimizing the KL divergence to the target distribution and demonstrate proof-of-concepts on text summarization and program synthesis tasks. For code generation, ILF improves a Codegen-Mono 6.1B model's pass@1 rate by 38% relative (and 10% absolute) on the Mostly Basic Python Problems (MBPP) benchmark, outperforming both fine-tuning on MBPP and fine-tuning on repaired programs written by humans. For summarization, we show that ILF can be combined with learning from human preferences to improve a GPT-3 model's summarization performance to be comparable to human quality, outperforming fine-tuning on human-written summaries. Overall, our results suggest that learning from human-written natural language feedback is both more effective and sample-efficient than training exclusively on demonstrations for improving an LLM's performance on a variety of tasks.

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ICML Conference 2023 Conference Paper

Do the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the Machiavelli Benchmark

Alexander Pan
Jun Shern Chan
Andy Zou
Nathaniel Li
Steven Basart
Thomas Woodside
Hanlin Zhang 0002
Scott Emmons

Artificial agents have traditionally been trained to maximize reward, which may incentivize power-seeking and deception, analogous to how next-token prediction in language models (LMs) may incentivize toxicity. So do agents naturally learn to be Machiavellian? And how do we measure these behaviors in general-purpose models such as GPT-4? Towards answering these questions, we introduce Machiavelli, a benchmark of 134 Choose-Your-Own-Adventure games containing over half a million rich, diverse scenarios that center on social decision-making. Scenario labeling is automated with LMs, which are more performant than human annotators. We mathematize dozens of harmful behaviors and use our annotations to evaluate agents’ tendencies to be power-seeking, cause disutility, and commit ethical violations. We observe some tension between maximizing reward and behaving ethically. To improve this trade-off, we investigate LM-based methods to steer agents towards less harmful behaviors. Our results show that agents can both act competently and morally, so concrete progress can currently be made in machine ethics–designing agents that are Pareto improvements in both safety and capabilities.

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NeurIPS Conference 2022 Conference Paper

How Would The Viewer Feel? Estimating Wellbeing From Video Scenarios

Mantas Mazeika
Eric Tang
Andy Zou
Steven Basart
Jun Shern Chan
Dawn Song
David Forsyth
Jacob Steinhardt

In recent years, deep neural networks have demonstrated increasingly strong abilities to recognize objects and activities in videos. However, as video understanding becomes widely used in real-world applications, a key consideration is developing human-centric systems that understand not only the content of the video but also how it would affect the wellbeing and emotional state of viewers. To facilitate research in this setting, we introduce two large-scale datasets with over 60, 000 videos manually annotated for emotional response and subjective wellbeing. The Video Cognitive Empathy (VCE) dataset contains annotations for distributions of fine-grained emotional responses, allowing models to gain a detailed understanding of affective states. The Video to Valence (V2V) dataset contains annotations of relative pleasantness between videos, which enables predicting a continuous spectrum of wellbeing. In experiments, we show how video models that are primarily trained to recognize actions and find contours of objects can be repurposed to understand human preferences and the emotional content of videos. Although there is room for improvement, predicting wellbeing and emotional response is on the horizon for state-of-the-art models. We hope our datasets can help foster further advances at the intersection of commonsense video understanding and human preference learning.

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