author
Chief Scientist
,
Snorkel AI
Assistant Professor @ University of Wisconsin-Madison
Frederic Sala is Chief Scientist at Snorkel AI and an assistant professor in the Computer Sciences Department at the University of Wisconsin-Madison. His research studies the fundamentals of data-driven systems and machine learning, with a focus on data-centric AI, foundation models, and automated machine learning. He and his group received the 2024 DARPA Young Faculty Award, a best student paper runner-up award at UAI ’22, the outstanding Ph.D. dissertation award from the UCLA Department of Electrical Engineering, the NSF Graduate Research Fellowship.
The latest from Fred
Models Can Model, But Can’t Bind: Structured Grounding in Text-to-Optimization
Text-to-optimization requires two separable capabilities: modeling -- choosing the right optimization structure -- and binding -- grounding every coefficient, index, and parameter in the concrete problem data. We study this via Text2Opt-Bench, a scalable benchmark of solver-verified optimization problems spanning 12 categories, from textbook linear programs to stochastic and multi-objective formulations with up to thousands of variables. Across 10+ models, we find that accuracy collapses as instance data grows, even when the formulation itself is simple. We call this the effective binding limit. We study it with a family of techniques, BIND, that externalize numeric data to structured files so...
Research Paper
Accepted to COLM
Models Can Model, But Can’t Bind: Structured Grounding in Text-to-Optimization
Text-to-optimization requires two separable capabilities: modeling — choosing the right optimization structure — and binding — grounding every coefficient, index, and parameter in the concrete problem data. We study this via Text2Opt-Bench, a scalable benchmark of solver-verified optimization problems spanning 12 categories, from textbook linear programs to stochastic and multi-objective formulations with up to thousands of variables. Across 10+ models,…
OSWorld 2.0: Benchmarking Computer Use Agents on Long-Horizon Real-World Tasks
Existing computer-use benchmarks fail to capture the realism, complexity, and long-horizon demands of real-world computer use, limiting their ability to reveal the limita-tions of frontier agents. We introduce OSWORLD 2.0, a benchmark of 108 long-horizoncomputer-use workflows across everyday and professional tasks, designed to capturecomplex and challenging real-world phenomena. Each task represents a realistic end-to-end workflow that takes human users a median of about 1.6 hours to complete andrequires an average of 318 tool calls with Claude Opus 4.7 using maximum thinking,compared with about 30 in OSWORLD 1.0. OSWORLD 2.0 targets challenge phenomenathat are common in real workflows yet underrepresented in...
Research Paper
OSWorld 2.0: Benchmarking Computer Use Agents on Long-Horizon Real-World Tasks
Existing computer-use benchmarks fail to capture the realism, complexity, and long-horizon demands of real-world computer use, limiting their ability to reveal the limita-tions of frontier agents. We introduce OSWORLD 2.0, a benchmark of 108 long-horizoncomputer-use workflows across everyday and professional tasks, designed to capturecomplex and challenging real-world phenomena. Each task represents a realistic end-to-end workflow that takes human users a…
Can Generalist Agents Automate Data Curation?
Curating training data is among the most consequential yet labor-intensive parts of modern AI development: practitioners iteratively propose, implement, evaluate, and revise data policies against noisy benchmark feedback. We ask whether generalist coding agents can automate this data-curation loop. We introduce CURATION-BENCH, an agent-centric benchmark that fixes the model, training recipe, and evaluation suite while giving agents commandline access to inspect data, implement policies, submit them to a fixed training/evaluation pipeline, and revise. In a vision-language instruction-tuning instantiation, out-of-the-box agents reach strong published data-selection baselines within ten iterations. However, trajectory analysis reveals a persistent execution– research gap: agents mainly tune...
Research Paper
Can Generalist Agents Automate Data Curation?
Curating training data is among the most consequential yet labor-intensive parts of modern AI development: practitioners iteratively propose, implement, evaluate, and revise data policies against noisy benchmark feedback. We ask whether generalist coding agents can automate this data-curation loop. We introduce CURATION-BENCH, an agent-centric benchmark that fixes the model, training recipe, and evaluation suite while giving agents commandline access to…
Evaluating Sample Utility for Efficient Data Selection by Mimicking Model Weights
Large-scale web-crawled datasets contain noise, bias, and irrelevant information, necessitating data selection techniques. Existing methods depend on hand-crafted heuristics, downstream datasets, or require expensive influence-based computations -- all of which limit scalability and introduce unwanted data dependencies. To address this, we introduce the Mimic Score, a simple and geometry-based data-quality metric that evaluates utility by measuring alignment between a sample's gradients and a target direction induced by a pre-trained reference model. This leverages readily available model weights, avoids needing validation datasets, and incurs minimal computational overheads. Building on this metric, we propose Grad-Mimic, a two-stage framework that re-weights samples online...
Research Paper
Evaluating Sample Utility for Efficient Data Selection by Mimicking Model Weights
Large-scale web-crawled datasets contain noise, bias, and irrelevant information, necessitating data selection techniques. Existing methods depend on hand-crafted heuristics, downstream datasets, or require expensive influence-based computations — all of which limit scalability and introduce unwanted data dependencies. To address this, we introduce the Mimic Score, a simple and geometry-based data-quality metric that evaluates utility by measuring alignment between a sample’s…
RIFT: A Rubric Failure Mode Taxonomy and Automated Diagnostics
Rubric-based evaluation is widely used in LLM benchmarks and training pipelines for open-ended, less verifiable tasks. While prior work has demonstrated the effectiveness of rubrics using downstream signals such as reinforcement learning outcomes, there remains no principled way to diagnose rubric quality issues from such aggregated or downstream signals alone. To address this gap, we introduce RIFT: RubrIc Failure mode Taxonomy, a taxonomy for systematically characterizing failure modes in rubric composition and design. RIFT consists of eight failure modes organized into three high-level categories: Reliability Failures, Content Validity Failures, and Consequential Validity Failures. RIFT is developed using grounded theory by...
Research Paper
Accepted to ICLR Brazil 2026
RIFT: A Rubric Failure Mode Taxonomy and Automated Diagnostics
Rubric-based evaluation is widely used in LLM benchmarks and training pipelines for open-ended, less verifiable tasks. While prior work has demonstrated the effectiveness of rubrics using downstream signals such as reinforcement learning outcomes, there remains no principled way to diagnose rubric quality issues from such aggregated or downstream signals alone. To address this gap, we introduce RIFT: RubrIc Failure mode…
Learning from Less: Measuring the Effectiveness of RLVR in Low Data and Compute Regimes
Fine-tuning Large Language Models (LLMs) typically relies on large quantities of high-quality annotated data, or questions with well-defined ground truth answers in the case of Reinforcement Learning with Verifiable Rewards (RLVR). While previous work has explored the benefits to model reasoning capabilities by scaling both data and compute used for RLVR, these results lack applicability in many real-world settings where annotated data and accessible compute may be scarce. In this work, we present a comprehensive empirical study of open-source Small Language Model (SLM) performance after RLVR in low data regimes. Across three novel datasets covering number counting problems, graph reasoning,...
Research Paper
Accepted to MLSys 2026
Learning from Less: Measuring the Effectiveness of RLVR in Low Data and Compute Regimes
Fine-tuning Large Language Models (LLMs) typically relies on large quantities of high-quality annotated data, or questions with well-defined ground truth answers in the case of Reinforcement Learning with Verifiable Rewards (RLVR). While previous work has explored the benefits to model reasoning capabilities by scaling both data and compute used for RLVR, these results lack applicability in many real-world settings where…
LiveResearchBench: A Live Benchmark for User-Centric Deep Research in the Wild
Deep research---producing comprehensive, citation-backed reports by searching across hundreds of live websites---marks an important frontier for agentic systems. To rigorously evaluate this ability, three principles are essential: tasks should be (1) user-centric, reflecting realistic information needs, (2) dynamic, requiring up-to-date information beyond parametric knowledge, and (3) unambiguous, ensuring consistent interpretation across users. Existing benchmarks fall short of these principles, often focusing on narrow domains or posing ambiguous questions that hinder fair comparison. Guided by these principles, we introduce LiveResearchBench, a benchmark of 100 expert-curated tasks spanning daily life, enterprise, and academia, each requiring extensive, dynamic, real-time web search and synthesis....
Research Paper
LiveResearchBench: A Live Benchmark for User-Centric Deep Research in the Wild
Deep research—producing comprehensive, citation-backed reports by searching across hundreds of live websites—marks an important frontier for agentic systems. To rigorously evaluate this ability, three principles are essential: tasks should be (1) user-centric, reflecting realistic information needs, (2) dynamic, requiring up-to-date information beyond parametric knowledge, and (3) unambiguous, ensuring consistent interpretation across users. Existing benchmarks fall short of these principles, often…
Test-Time Scaling Makes Overtraining Compute-Optimal
Modern LLMs scale at test-time, e.g. via repeated sampling, where inference cost grows with model size and the number of samples. This creates a trade-off that pretraining scaling laws, such as Chinchilla, do not address. We present Train-to-Test (T2) scaling laws that jointly optimize model size, training tokens, and number of inference samples under fixed end-to-end budgets. T2 modernizes pretraining scaling laws with pass@k modeling used for test-time scaling, then jointly optimizes pretraining and test-time decisions. Forecasts from T2 are robust over distinct modeling approaches: measuring joint scaling effect on the task loss and modeling impact on task accuracy. Across eight downstream tasks, we find...
Research Paper
Accepted to COLM
Test-Time Scaling Makes Overtraining Compute-Optimal
Modern LLMs scale at test-time, e.g. via repeated sampling, where inference cost grows with model size and the number of samples. This creates a trade-off that pretraining scaling laws, such as Chinchilla, do not address. We present Train-to-Test (T2) scaling laws that jointly optimize model size, training tokens, and number of inference samples under fixed end-to-end budgets. T2 modernizes pretraining scaling laws…
OR-LLM-Bench: A Pipeline for Scalable and Verifiable Text-to-Optimization Synthesis
Operations research (OR)-style modeling poses challenges for large language models (LLMs). It requires long-context consistency, producing precise mathematical formulations, and the ability to infer implicit constraints. To study these challenges under controlled conditions, we build a verifiable synthetic pipeline that generates large-scale certified optimization problem instances. Using this pipeline, we obtain several insights: first, direct natural language translation of optimization problems runs into an \emph{effective context limit}, beyond which frontier models abruptly fail to maintain global variable–constraint consistency---despite remaining within nominal context window length. Second, naive divide-and-conquer scaling strategies struggle due to context explosion and semantic fragmentation. Third, while frontier...
Research Paper
OR-LLM-Bench: A Pipeline for Scalable and Verifiable Text-to-Optimization Synthesis
Operations research (OR)-style modeling poses challenges for large language models (LLMs). It requires long-context consistency, producing precise mathematical formulations, and the ability to infer implicit constraints. To study these challenges under controlled conditions, we build a verifiable synthetic pipeline that generates large-scale certified optimization problem instances. Using this pipeline, we obtain several insights: first, direct natural language translation of optimization…
