AI EVALUATION • PRINCETON UNIVERSITY

MLRC 2025 at Princeton: five principles for reproducible LLM evaluation

At the 2025 Machine Learning Reproducibility Challenge, hosted by Princeton AI Lab, we explored a decisive question for responsible LLM adoption: how can teams know whether a result is robust, comparable, and reproducible—not merely a strong number observed once?

8 minPublished July 21, 2026

MLRC 2025 brought the community together at Princeton to discuss evaluation and reproducibility in machine learning.

Language models are probabilistic, depend on long software and hardware stacks, and respond to seemingly small details. Evaluation therefore looks more like a continuous experimental process than a one-time proof of performance.

Classes, presentations, and discussions at MLRC 2025 reinforced that AI quality requires method: record conditions, repeat experiments, quantify uncertainty, investigate errors, and treat benchmarks as instruments that must also evolve.

ARTIFICIAL INTELLIGENCE

Five lessons for teams building with LLMs

01
Reproducibility remains a central challenge
Small changes in prompts, examples, ordering, code, dependencies, or parameters can alter results. A reliable evaluation records versions and configurations, preserves inputs and outputs, and repeats runs enough to measure variation.
02
Benchmarks age alongside models
Practice moved from fine-tuning settings popularized in the BERT era to in-context learning and conversational reasoning. Older tests may saturate, leak into training data, or stop representing actual use.
03
Determinism has both cost and limits
Fully deterministic execution can be far slower—examples discussed reached roughly seven times the cost. Even with the same seed, CPU, GPU, kernel, or inference-mode differences may change outputs. The practical path is to measure distributions and document the environment.
04
Data access is now part of scientific risk
Previously open datasets may move behind expensive licenses or disappear, preventing third parties from reproducing state-of-the-art results. Data governance, preserved versions, and public alternatives belong in experimental design.
05
Sound practice matters more than a single record
Sharing code and outputs, applying bootstrap methods and confidence intervals, performing error analysis, and designing harder, well-annotated benchmarks yields better evidence than chasing one leaderboard position.

From demo to evaluation system

In enterprise projects, a compelling demonstration is not enough. Teams need representative tasks, success criteria, protected test sets, automated and human evaluation, risk segmentation, and monitoring after release.

Capabilities must also remain distinct: factual quality, instruction following, safety, latency, cost, stability, tool use, and user experience do not fit one metric. A single aggregate score can hide the behavior that matters most to the business.

How we apply these principles

At EAGLE BS, evaluation is part of AI architecture. Before scaling, we establish a baseline, record prompts, models, and versions, define critical cases, monitor regressions, and connect technical measures to expected user outcomes.

Reproducibility does not mean requiring every token to be identical. It means creating enough evidence to understand variation, repeat a decision, and explain why a solution is—or is not yet—ready for a particular context.

Learning from a rigorous community

We are grateful for the knowledge shared by Koustuv Sinha, Jessica Forde, Adina Williams, Michael Rabbat, Naila Murray, Joelle Pineau, Arvind Narayanan, Hugo Larochelle, Robert Stojnic, Jesse Dodge, Sasha Luccioni, Stella Biderman, and the wider community in attendance.

Our central takeaway is simple: the objective is not only the best number on a table, but evidence that is robust, transparent, explainable, and reproducible enough to support real decisions.