self-refinement
2 published articles
Research Digest
The simple topology trick that beats prompt magic in LLM induction tasks
A new methodology called Hourglass reasoning enforces strict context isolation between induction, deduction, and implementation stages, passing only a compressed symbolic rule between them. On ARC-AGI-2, it raises best-of-5 accuracy by up to 14 points over iterative refinement; on ChipBench Verilog synthesis, it nearly doubles accuracy with GPT-5.5. Ablations confirm the topology itself drives the improvement.
2026-07-31
RefineRL skeptical loop pushes 4B models past 32B rivals
The 4B model that beats 32B ones by refusing to trust itself
RefineRL trains small language models to iteratively refine their own competitive programming solutions using a skeptical agent and reinforcement learning. A 4B model using this method outperforms 32B models and approaches 235B performance, suggesting that self-refinement, not raw size, may be a stronger scaling path for reasoning tasks.
2026-07-25