US EditionHigh-Entropy Token Selection in Reinforcement Learning with Verifiable Rewards (RLVR) Improves Accuracy and Reduces Training Cost for LLMs
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High-Entropy Token Selection in Reinforcement Learning with Verifiable Rewards (RLVR) Improves Accuracy and Reduces Training Cost for LLMs – #CryptoUpdatesGNIT
Large Language Models (LLMs) generate step-by-step responses known as Chain-of-Thoughts (CoTs), where each token contributes to a coherent and logical narrative. To improve the quality of reasoning, various reinforcement learning techniques have been employed. These methods allow the model to learn from feedback mechanisms by aligning generated outputs with correctness criteria. As LLMs grow in complexity and capacity, researchers have begun pro…
High-Entropy Token Selection in Reinforcement Learning with Verifiable Rewards (RLVR) Improves Accuracy and Reduces Training Cost for LLMs
Large Language Models (LLMs) generate step-by-step responses known as Chain-of-Thoughts (CoTs), where each token contributes to a coherent and logical narrative. To improve the quality of reasoning, various reinforcement learning techniques have been employed. These methods allow the model to learn from feedback mechanisms by aligning generated outputs with correctness criteria. As LLMs grow in complexity and capacity, researchers have begun pro…
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