author
Staff Research Scientist
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Snorkel AI
Tom Walshe is a Staff Research Scientist at Snorkel AI. Before Snorkel, Tom worked in LegalTech and finance services, where he focussed on building end-to-end AI systems and researching data-centric AI. Prior to industry, Tom completed a PhD in Computer Science from the University of Oxford.
The latest from Tom Walshe
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…
Automating benchmark design
The rapid progress and widespread deployment of LLMs and LLM-powered agents has outpaced our ability to evaluate them. Hand-crafted, static benchmarks are the primary tool for assessing model capabilities, but these quickly become saturated. In contrast, dynamic benchmarks evolve alongside the models they evaluate, but are expensive to create and continuously update. To address these challenges, we develop BeTaL (Benchmark Tuning with an LLM-in-the-loop), a framework that leverages environment design principles to automate the process of dynamic benchmark design. BeTaL works by parameterizing key design choices in base benchmark templates and uses LLMs to reason through the resulting parameter space...
Research Paper
Automating benchmark design
The rapid progress and widespread deployment of LLMs and LLM-powered agents has outpaced our ability to evaluate them. Hand-crafted, static benchmarks are the primary tool for assessing model capabilities, but these quickly become saturated. In contrast, dynamic benchmarks evolve alongside the models they evaluate, but are expensive to create and continuously update. To address these challenges, we develop BeTaL (Benchmark…
Blog
Evaluating coding agent capabilities with Terminal-Bench: Snorkel’s role in building the next generation benchmark
Terminal-Bench, developed through a collaboration between Stanford University and Laude Institute, has quickly become the gold standard benchmark for evaluating AI agent capabilities in a command line environment. This comprehensive evaluation framework measures how effectively AI agents can perform complex, real-world tasks within terminal environments. At Snorkel AI, we’re excited to share that we’re one of the top collaborators contributing…
Blog
The right tool for the job: An A-Z of rubrics
Rubrics turn fuzzy “good vs. bad” into measurable criteria for GenAI. In Part 2, we map what to measure (granularity and dataset-level vs instance-specific), where to measure (process vs outcome), and how to measure (humans, LLM-as-judge, code, reward models)—with examples like HHH, FLASK, HealthBench, and PaperBench.
Blog
LLM alignment techniques: 4 post-training approaches
Ensure your LLMs align with your values and goals using LLM alignment techniques. Learn how to mitigate risks and optimize performance.
Blog
Walking safely before building flying saucer seatbelts: introducing Enterprise Alignment
Snorkel takes a step on the path to enterprise superalignment with new data development workflows for enterprise alignment
