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GRPO is a reinforcement learning method that requires no value network; it updates the policy by sampling a group of responses to the same prompt and using the within-group relative advantage as the reward signal, and it performs well on math and reasoning tasks, but its reward signal itself carries no geometric information.
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10/15/2026
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VerifiedGRPO (Group Relative Policy Optimization) is an improved variant of PPO that requires no additional value network training; instead, it calculates the reward signal by sampling a group of responses to the same question and using intra-group relative performance as a baseline.80% similarUnverifiedThe team proposed the Faithful GRPO method, which introduces consistency rewards and grounding rewards and balances constraints by learning Lagrangian multiplier coefficients, in order to maintain reasoning faithfulness while improving performance76% similarVerifiedAt the training level, reasoning-enhanced models typically leverage reinforcement learning algorithms such as GRPO (Group Relative Policy Optimization), using the correctness of the final answer as the reward signal74% similarUnverifiedWhen GRPO is used for long-form RAG training, the answer-level scalar advantage value is evenly distributed to each sentence in the answer, causing copied sentences and genuinely grounded sentences to receive the same reinforcement signal.74% similarUnverifiedJustGRPO的核心修复方案是在自回归顺序下训练GRPO，推理阶段保持并行解码71% similar
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