State of RL for Reasoning LLMs

🔑 Enhanced Key Takeaways

• •Reinforcement Learning from Verifiable Rewards (RLVR) has emerged as a 2026 standard for training LLMs on objectively verifiable tasks like math and code, rewarding correctness over plausibility.[3][5]
• •OpenAI's o3 reasoning model utilized 10x more training compute than o1 via RL methods, highlighting ongoing scaling benefits for reasoning capabilities.[4]
• •REINFORCE offers a simpler, cheaper alternative to PPO for online RL in LLMs, using bandit or per-token MDP formulations without complex constraints.[6]
• •DeepSeek-R1 demonstrated that SFT followed by RL outperforms RL alone, with distilled models gaining further from additional RL on modest compute.[4]

🛠️ Technical Deep Dive

• •RLHF employs PPO with human preference reward models; RLVR uses rule-based verifiers for tasks like math/code, enabling verifiable rewards without human input.[6]
• •REINFORCE derives policy gradients for online RL: basic form updates policy via ∇θ J(θ) = E[∇θ log πθ(a|s) * (R - b)], avoiding PPO's clipping and value function.[6]
• •GRPO pairs with RLVR for cost-effective scaling, updating value models alongside policies in multi-model setups (e.g., three 600B-param models).[5]
• •Microsoft's analysis shows policy gradient (PG) with 0-1 rewards equates to SFT on exploration data per iteration, yet outperforms due to diverse sampling.[2]

🔮 Future ImplicationsAI analysis grounded in cited sources

⏳ Timeline