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Models & Algorithms
Reinforcement Learning & Planning Systems
Overview
Unlike autoregressive models that guess the next word immediately, reasoning systems leverage reinforcement learning during training to plan, self-correct, and search through multiple answer paths. By running 'chains of thought' behind the scenes, these models can solve highly complex scientific and mathematical problems.
Role in the Stack
Standard LLMs struggle with multi-step logical tasks because single error propagation derails the entire response. Reasoning systems bypass this by planning and verifying their steps.
Key Pioneers & Stakeholders
OpenAI (o1)
Google DeepMind (AlphaProof / AlphaGo)
Meta (System 2 research)
Stanford (STaR framework)
Key Statistics & Targets
Silver medal equivalent
Math Olympiad score (AlphaProof)
Proportional to thinking time
Inference compute scaling
Record-high resolution rates
Coding Benchmark (SWE-bench)
Future Industry Directions
1
Test-time compute scaling
Allowing users to pay for more 'thinking time' to solve extremely hard problems.
2
Self-improving loops
Models generating their own synthetic training data, filtering it via verification solvers, and self-training.