How GRPO Trains Small Language Models with Verifiable Rewards
Branching paths represent possible responses, while the highlighted orange route symbolizes an answer that passes verification and receives a positive reward.A language model can write "let me double-check that" and still get the multiplication wrong. It can even write "wait, let me reconsider," and land on a different wrong answer. If the goal is solving the problem, only the number at the end counts.DeepSeek’s R1-Zero brought considerable attention to reinforcement learning without a preliminary supervised fine-tuning stage.
- ▪Branching paths represent possible responses, while the highlighted orange route symbolizes an answer that passes verification and receives a positive reward.A language model can write "let me double-check that" and still get the multiplica
- ▪It can even write "wait, let me reconsider," and land on a different wrong answer.
- ▪If the goal is solving the problem, only the number at the end counts.DeepSeek’s R1-Zero brought considerable attention to reinforcement learning without a preliminary supervised fine-tuning stage.
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| Publication time | Wed, 23 Sep 2026 12:30:01 GMT |
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Machine LearningHow GRPO Trains Small Language Models with Verifiable RewardsThe mechanics behind local reasoning experiments with Unsloth and why the reward function matters as much as the model.Benjamin NwekeSeptember 23, 202610 min readImage by author (Generated with ChatGPT)Conceptual illustration of GRPO reinforcement learning for small language models. Branching paths represent possible responses, while the highlighted orange route symbolizes an answer that passes verification and receives a positive reward.A language model can write "let me double-check that" and still get the multiplication wrong. It can even write "wait, let me reconsider," and land on a different wrong answer. Neither sentence is evidence of thinking.
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