author
Sr. Director, R&D
,
Snorkel AI
Armin Parchami is the Senior Director, R&D, at Snorkel AI, where he leads work on synthetic data, data quality, and model fine-tuning. He previously held technical leadership roles at Ford and Nokia Bell Labs, focusing on multimodal AI and autonomy. His work centers on moving research into production.
The latest from Armin Parchami
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…
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…
Blog
The self-critique paradox: Why AI verification fails where it’s needed most
TL;DR: We stress-tested the “generate → criticize → improve” loop on 50 visual reasoning tasks. The results were counterintuitive: self-critique acts as a corrosive agent on high-performance tasks, turning 98% accuracy into 57%. Yet, for tasks where models fail completely, it works like magic. This difficulty-dependent behavior poses a critical, hidden risk for RLFT pipelines. The promise vs. the reality…
Blog
Snorkeling in RL environments
We unpack what makes a high-quality RL environment for LLMs and show how we build realistic, enterprise-grade environments at Snorkel AI.
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
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
Data quality and rubrics: how to build trust in your models
Rubrics aren’t just for evaluation—they’re a blueprint for better data annotation. In this post, we explore how structured rubrics enable scalable, high-quality labeling and evaluation of GenAI systems. Learn how Snorkel and leading labs use rubrics to align human and automated judgment and accelerate trusted AI development.
