Implicit Actor Critic Coupling via a Supervised Learning Framework for RLVR (arxiv.org)

Researchers present a model-agnostic verification-and-refinement pipeline that dramatically boosts large language model performance on Olympiad-level math: applied to IMO 2025 (with no data contamination for models released before the contest), the pipeline enabled Gemini 2.5 Pro, Grok-4, and GPT-5 to collectively solve 5 of 6 problems (~85.7% accuracy). That contrasts sharply with baseline performance obtained by naively selecting the best of 32 candidate solutions: 31.6% (Gemini 2.5 Pro), 21.4% (Grok-4), and 38.1% (GPT-5). The work shows that careful prompting plus structured verification and iterative refinement — rather than only larger or newer base models — can unlock far stronger reasoning on hard, novelty-driven tasks. Technically, the approach is model-agnostic: it uses tailored prompts to generate candidate solutions, applies verification checks to identify flaws, and then refines or re-generates solutions until correctness criteria are met. The result underscores two key implications for the AI/ML community: first, modular pipelines (generation + verifier + refiner) are a powerful lever for scientific and mathematical reasoning; second, progress in demanding domains may hinge as much on verification frameworks and process design as on raw model scale. This suggests future directions in automated proof verification, verifier-model training, and robust refinement strategies for complex problem solving.