Why Simple Rules Beat Value Models in Reasoning RL

Reinforcement learning with verifiable rewards powers modern reasoning models without requiring complex neural critics. By replacing a policy-sized value model with group-relative advantage estimation, algorithms like GRPO cut memory overhead and avoid reward hacking. A look at the inner mechanics of rule-based scoring, how crude outcome verification generates tens of thousands of reasoning tokens, and why mathematical baselines prove this group comparison matches an oracle critic in the limit. Key sources: • DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models (Shao et al.), v3 revised 2024-04-27; §4.1 GRPO objective, Algorithm 1, §4.1.2 — https://arxiv.org/abs/2402.03300v3 • DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning (DeepSeek-AI), v2 revised 2026-01-04; published *Nature* 645:633 — https://arxiv.org/abs/2501.12948v2 • Demystifying Group Relative Policy Optimization: Its Policy Gradient is a U-Statistic (Zhou et al.), revised 2026-03-22. retrieved 2026-11-08 — https://arxiv.org/abs/2603.01162v3 Episode 6499 — myweirdprompts.com/n/6499 (permanent short link: myweirdprompts.com/621436) Listen on Android: MWP FM Player — myweirdprompts.com/apk