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135        <span class="post-meta">Aug 24, 2026</span><span class="post-meta"> • Read time ~22 min</span><h3 style="margin: 5px 0px 7px 0px">
136          <a class="post-link" href="/post-training/2026/08/24/post-training-primer.html"><span class="link-highlights">Post-Training Primer</span></a>
137        </h3><p style="font-size: 14px; color: rgba(193, 215, 240, 0.39); text-align: left;">A first-principles look at post-training for language models, tracing how policy gradients lead to PPO and GRPO. The post breaks down reward-to-go, advantage estimation, probability-ratio clipping, and group-relative rewards, while highlighting the practical differences between PPO’s learned critic and GRPO’s critic-free approach. It also covers implementation details and common pitfalls when putting these methods into practice.</p></li>
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141        <span class="post-meta">Aug 13, 2026</span><span class="post-meta"> • Read time ~9 min</span><h3 style="margin: 5px 0px 7px 0px">
142          <a class="post-link" href="/uncertainty/2026/08/13/uncertainty-estimation-part-2.html"><span class="link-highlights">Uncertainty in Deep Learning, p.2 (Methods)</span></a>
143        </h3><p style="font-size: 14px; color: rgba(193, 215, 240, 0.39); text-align: left;">In this post, we explores 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.</p></li>
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147        <span class="post-meta">Mar 8, 2025</span><span class="post-meta"> • Read time ~13 min</span><h3 style="margin: 5px 0px 7px 0px">
148          <a class="post-link" href="/uncertainty/2025/03/08/uncertainty-estimation-intro.html"><span class="link-highlights">Uncertainty in Deep Learning, p.1 (Intro)</span></a>
149        </h3><p style="font-size: 14px; color: rgba(193, 215, 240, 0.39); text-align: left;">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.</p></li>
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