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We investigate how various reward signals, even spurious and random ones, impact the performance of different language models fine-tuned for mathematical reasoning using Reinforcement Learning from Verbose Reasoning (RLVR). The research demonstrates that while Qwen models show significant improvement even with weak or incorrect rewards, this benefit is not universal, with Llama and OLMo models

Qwen 2.5, RL, and Random Rewards

Best AI papers explained