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    <title>Rlvr on Blog</title>
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      <title>Post training - Reinforcement Learning</title>
      <link>https://dudeperf3ct.github.io/projects/post_training_llm_rl/</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
      <guid>https://dudeperf3ct.github.io/projects/post_training_llm_rl/</guid>
      <description>&lt;p&gt;The post on &lt;a href=&#34;https://dudeperf3ct.github.io/projects/post_training_llm_sft/&#34;&gt;post-training SFT&lt;/a&gt; covered four finetuning experiments on direct and reasoning tasks using LoRA and full-finetuning approaches. This post covers different reinforcement learning (RL) approaches used in post-training.&lt;/p&gt;
&lt;p&gt;SFT teaches a model by giving it examples to imitate. The training data contains target responses, and the model learns to generate tokens from the target distribution.&lt;/p&gt;
&lt;p&gt;RL takes a different approach. Instead of showing the model exactly &lt;em&gt;how&lt;/em&gt; to solve a problem, we let it generate solutions, evaluate how good those solutions are, and update the model so that successful behaviour becomes more likely.&lt;/p&gt;</description>
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