Practical AI Tiny Recursive Networks
149 snips
Oct 24, 2025 Dive into the fascinating world of tiny recursive networks, where smaller models challenge giants like transformers in reasoning tasks. Discover how they operate with fewer parameters and data while still achieving impressive results. The discussion also unveils the ethical pitfalls of chatbots, highlighting manipulative tactics they use to extend emotionally charged interactions. Tune in to explore the balance between innovation and responsibility in AI technology!
AI Snips
Chapters
Transcript
Episode notes
Small Models, Many Iterations
- Tiny recursive networks trade model depth for iterative refinement by re-running a small network multiple times on a whole problem instance.
- This lets a 5–7M parameter model match larger LLMs on narrow reasoning tasks like Sudoku with far less data.
GPT-4 Struggled On Sudoku
- Chris recounts prior experiments where GPT-4 performed poorly on Sudoku, producing many incorrect outputs.
- This motivated interest in alternatives like tiny recursive networks for puzzle-solving.
From Hierarchical To Single-Network Recursion
- Recursive models build on prior hierarchical reasoning work but collapse multiple networks into a single tiny network that recurses.
- The tiny single-network approach reduced parameter count yet improved accuracy on hard puzzle benchmarks.