AI Coding Live News

On 2026-10-06 OpenAI published Sharing AI progress in mathematics and released openai/math (Apache-2.0): 722 manuscripts in 372 result families produced by an unreleased internal frontier model, drawn from an evaluation of ~4,000 open problems at an average of ~3 hours of ChatGPT Pro thinking compute per result, with 10 abridged reasoning summaries and Lean formalizations for many results. OpenAI notes unformalized results may have issues; none are peer reviewed yet.

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While reinforcement learning produces distinct specialized expert models excels at mathematics, code synthesis, or instruction following, real-world deployment requires a single unified foundation model possessing all of these capabilities. Multi-teacher on-policy distillation (MOPD) aims to merge these capabilities by routing student-sampled prompts to domain specialists for token-level supervision. However, researchers discover an acute imbalance across Qwen3.5 architectures: instruction-following teachers exhibit broad distribution dispersion, generating gradient magnitudes that overwhelm dense mathematical signals and degrading mathematical reasoning. The team introduces Domain-Normalized MOPD (DN-MOPD, arXiv:2609.35347), which normalizes each domain's supervisory signal by its measured empirical spread. Across six public benchmarks, DN-MOPD consistently outperforms standard MOPD across all model scales, recovering mathematical reasoning performance without sacrificing instruction compliance.

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Multi-teacher on-policy distillation (MOPD) aims to synthesize diverse capabilities from specialized models (mathematics, coding, instruction-following) into a single versatile student. However, empirical studies reveal that standard MOPD students fail to beat single-specialist baselines and lose their math edge because instruction-following feedback is exponentially more dispersed and dominates student gradient updates. Researchers introduced Domain-Normalized MOPD (DN-MOPD), which rescales feedback by measured domain spread to recover math and coding expertise across six public benchmarks.

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Researchers from New York University in collaboration with Google DeepMind's Rémi Munos have published 'Learning to Discover Interesting Mathematics' (arXiv: 2609.28603). While LLMs excel at proving existing conjectures, autonomous mathematical expansion has been crippled by models spamming trivial or useless lemmas. The authors formulate a metric for intrinsic mathematical interestingness—defined by the compression ratio between proof length and statement brevity. Powered by a specialized 27B proof-difficulty model, the framework autonomously conjectures, verifies, and curates high-value theorems, enabling self-expanding formal mathematical libraries without human-specified goals.

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