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@@ -14,7 +14,17 @@
       <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, 02 Jun 2025 14:26:51 +0000</lastBuildDate>
+    <lastBuildDate>Tue, 03 Jun 2025 14:27:21 +0000</lastBuildDate>
+    <item>
+      <title>High-entropy minority tokens drive effective RLVR for LLM models.</title>
+      <description>We're about to dive into the uncharted territory of Reinforcement Learning with Verifiable Rewards (RLVR) and explore how a select group of tokens with high entropy can dramatically improve the reasoning capabilities of Large Language Models. By examining token entropy patterns in Chain-of-Thought (CoT) reasoning, we'll uncover the crucial role these high-entropy minority tokens play in steering model behavior and discover a novel approach to optimizing RLVR training.
+
+&lt;a href="https://huggingface.co/papers/2506.01939"&gt;[Read the paper on Hugging Face]&lt;/a&gt;</description>
+      <pubDate>Tue, 03 Jun 2025 14:27:21 +0000</pubDate>
+      <enclosure url="https://huggingface.co/spaces/fdaudens/podcast-jobs/resolve/main/podcasts/podcast-2025-06-03.wav" length="8745644" type="audio/wav" />
+      <guid>https://huggingface.co/spaces/fdaudens/podcast-jobs/resolve/main/podcasts/podcast-2025-06-03.wav</guid>
+      <itunes:explicit>false</itunes:explicit>
+      </item>
     <item>
       <title>Does RL Truly Unlock New Reasoning Capabilities in Large Language Models?</title>
       <description>We explore the limitations of past research and challenge prevailing assumptions about reinforcement learning (RL) in large language models, demonstrating that prolonged RL training can genuinely expand a model's reasoning capabilities and unveil novel solution pathways, not just optimize existing knowledge.