THE BITTER LESSON FOR RL: VERIFICATION AS THE KEY TO REASONING LLMS

Event: AI/ML Conference Session · Duration: 31 min · ▶ Watch on YouTube

Abstract

This presentation delves into ‘The Bitter Lesson’ of AI, highlighting the exponential decrease in compute cost and the effectiveness of scalable methods like search and learning. It posits that verification is fundamental for enabling reasoning in Large Language Models (LLMs) and for scaling Reinforcement Learning (RL) approaches. The talk introduces generative verifiers, which frame verification as a reasoning problem, demonstrating superior test-time scaling and generalization compared to traditional discriminative verifiers. Furthermore, it discusses the potential of self-verification to unify LLM reasoners and verifiers, leading to improved performance and generalization across various domains, including those beyond human-generated prompts.

Speakers

  • Rishabh Agarwal — N/A

Talks (1)

  • 00:00:00 — Rishabh Agarwal: THE BITTER LESSON FOR RL: VERIFICATION AS THE KEY TO REASONING LLMS
    • This talk explores how the ‘Bitter Lesson’ of AI, emphasizing computation and scalable methods, applies to Large Language Models (LLMs) and argues that verification is the crucial bottleneck and key to advancing reasoning capabilities in LLMs.

Key Takeaways

  • General methods that leverage computation, particularly search and learning, are consistently the most effective approaches in AI, a principle known as ‘The Bitter Lesson’.
  • The exponential decrease in compute cost necessitates scalable methods, and verification is identified as the primary bottleneck for scaling Reinforcement Learning (RL) in Large Language Models (LLMs).
  • Generative verifiers, which treat verification as a reasoning problem and utilize LLM’s text generation capabilities, offer significant advantages in test-time scaling, data efficiency, and generalization over traditional discriminative verifiers.
  • Unifying LLM reasoners and verifiers through self-verification enables better test-time scaling and improved out-of-domain generalization, even for complex tasks like physics problems.
  • Future challenges include scaling RL to non-verifiable domains using generative verifiers, addressing reward underspecification (getting correct answers with incorrect reasoning), and achieving robust generalization beyond training domains.

Methods / Models / Datasets Mentioned

  • Monte Carlo Tree Search (MCTS)
  • AlphaGo
  • AlphaGo Zero
  • DeepSeek-R1-Zero
  • GenRM
  • GenRM-CoT
  • ThinkPRM
  • DiscPRM
  • LLM-as-a-Judge
  • Self-Consistency
  • DPO
  • GRPO
  • SFT
  • Weighted Voting
  • Majority Voting

Topics

The Bitter Lesson · Reinforcement Learning (RL) · Large Language Models (LLMs) · Verification · Reasoning · Computational Scaling · Generative Verifiers · Chain-of-Thought (CoT) · Test-Time Compute · Self-Verification · Reward Underspecification · Exploration

Notes

Open for commentary — connections to other work, critiques, follow-up reading.