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Community release of REAP-pruned Nemotron-3-Super-120B to 64B, GRPO fine-tuned on math, quantized to AWQ/FP8, hitting 90%+ on AIME 2026 and runnable on a single H100/RTX PRO 6000.
TabularMath introduces a benchmark and AutoT2T framework for evaluating LLMs' mathematical reasoning over tabular data, revealing that table complexity, data quality, and modality significantly impact model performance. The study addresses a gap in LLM evaluation by systematically assessing robustness to incomplete or inconsistent table information in real-world scenarios.
This paper introduces Adaptive Tool Trust Calibration (ATTC), a framework that improves tool-integrated reasoning models by enabling them to adaptively decide when to trust or ignore tool results based on code confidence scores. The approach addresses the "Tool Ignored" problem where models incorrectly dismiss correct tool outputs, achieving 4.1-7.5% performance improvements across multiple models and datasets.
OpenAI trained a system using verifiers to solve grade school math word problems with 90% of child-level accuracy, nearly doubling fine-tuned GPT-3 performance. The approach addresses language models' weakness in multistep reasoning by training verifiers to evaluate candidate solutions and select the best one.