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Oliver Jaffe

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3 papers

1 author row

ICLR Conference 2025 Conference Paper

AI Sandbagging: Language Models can Strategically Underperform on Evaluations

Teun van der Weij
Felix Hofstätter
Oliver Jaffe
Samuel F. Brown
Francis Rhys Ward

Trustworthy capability evaluations are crucial for ensuring the safety of AI systems, and are becoming a key component of AI regulation. However, the developers of an AI system, or the AI system itself, may have incentives for evaluations to understate the AI's actual capability. These conflicting interests lead to the problem of *sandbagging* – which we define as *strategic underperformance on an evaluation*. In this paper we assess sandbagging capabilities in contemporary language models (LMs). We prompt frontier LMs, like GPT-4 and Claude 3 Opus, to selectively underperform on dangerous capability evaluations, while maintaining performance on general (harmless) capability evaluations. Moreover, we find that models can be fine-tuned, on a synthetic dataset, to hide specific capabilities unless given a password. This behaviour generalizes to high-quality, held-out benchmarks such as WMDP. In addition, we show that both frontier and smaller models can be prompted or password-locked to target specific scores on a capability evaluation. We have mediocre success in password-locking a model to mimic the answers a weaker model would give. Overall, our results suggest that capability evaluations are vulnerable to sandbagging. This vulnerability decreases the trustworthiness of evaluations, and thereby undermines important safety decisions regarding the development and deployment of advanced AI systems. See our code at https://github.com/TeunvdWeij/sandbagging

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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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