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Post-Training Primer
A first-principles guide to language-model post-training. Shared probability and policy-gradient identities lead to supervised fine-tuning, reward modeling, DPO, PPO, GRPO, interactive agent training, and on-policy distillation, with detailed derivations, practical recipes, and an integrated reading guide.
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Uncertainty in Deep Learning, p.2 (Methods)
In this post, we explore the main methods for estimating uncertainty in deep learning — from Bayesian inference and sampling-based ensembles to efficient deterministic and calibration techniques. We discuss how each approach captures different types of uncertainty, their computational trade-offs, and when to prefer one over another in practice.
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Uncertainty in Deep Learning, p.1 (Intro)
Advances in deep learning have solved complex problems across multiple domains, aided by increased computational access. Yet, understanding the inner workings of these models remains a challenge, with uncertainty estimation providing some insights. In this blog post, we explore the basics of uncertainty estimation and how it makes AI smarter and more reliable.
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