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Perspectives on AI evaluation, post-training, and frontier model development.

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Evaluation Benchmarks AI Infrastructure

Why Academic Benchmarks Are Not Enough

A 90% accuracy rate in a real-world workflow does not mean 90% usable. It often means unusable.

Kulta Lab Mar 15, 2026
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RL Post-Training Infrastructure

The RL Environment as Infrastructure

Post-training data directly defines what the model learns. The RL environment is the decisive lever.

Kulta Lab Mar 01, 2026
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Flywheel Strategy Products

From Evaluation to Training: The Flywheel Effect

Benchmark evaluation surfaces model failure. Post-training environments address those failures. Each cycle compounds.

Kulta Lab Feb 15, 2026
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