Merge branch 'main' of hf.co:spaces/fdaudens/podcast-jobs

.gitattributes

@@ -49,3 +49,4 @@ podcasts/podcast-2025-05-14.wav filter=lfs diff=lfs merge=lfs -text
 podcasts/podcast-2025-05-15.wav filter=lfs diff=lfs merge=lfs -text
 podcasts/podcast-2025-05-16.wav filter=lfs diff=lfs merge=lfs -text
 podcasts/podcast-2025-05-19.wav filter=lfs diff=lfs merge=lfs -text
+podcasts/podcast-2025-05-20.wav filter=lfs diff=lfs merge=lfs -text

podcasts/podcast-2025-05-20.wav

@@ -0,0 +1,3 @@
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+size 12921644

rss.xml

@@ -14,7 +14,15 @@
       <itunes:email>florent.daudens@hf.co</itunes:email>
       </itunes:owner>
     <itunes:image href="https://huggingface.co/spaces/fdaudens/podcast-jobs/resolve/main/images/cover3.png" />
-    <lastBuildDate>Mon, 19 May 2025 13:45:57 +0000</lastBuildDate>
+    <lastBuildDate>Tue, 20 May 2025 18:46:38 +0000</lastBuildDate>
+    <item>
+      <title>Solving the Scaling Paradox with Chain-of-Language-Model</title>
+      <description>We'll explore the groundbreaking concept of Chain-of-Language-Model (CoLM) which offers a novel approach to scale up language models while preserving training efficiency and enabling elastic inference by integrating multi-scale training within a single forward propagation.</description>
+      <pubDate>Tue, 20 May 2025 18:46:38 +0000</pubDate>
+      <enclosure url="https://huggingface.co/spaces/fdaudens/podcast-jobs/resolve/main/podcasts/podcast-2025-05-20.wav" length="12921644" type="audio/wav" />
+      <guid>https://huggingface.co/spaces/fdaudens/podcast-jobs/resolve/main/podcasts/podcast-2025-05-20.wav</guid>
+      <itunes:explicit>false</itunes:explicit>
+      </item>
     <item>
       <title>Beyond Emergent 'Aha!' Moments: Unlocking Reliable Reasoning in Large Models</title>
       <description>We explore a novel approach to boost the reliability and scalability of large reasoning models by explicitly aligning them with classical reasoning meta-abilities. Our three-stage pipeline improves performance by over 10% and enables domain-specific reinforcement learning from aligned checkpoints, demonstrating a scalable and dependable foundation for reasoning.</description>