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This paper introduces a methodology to enrich scientific logicality in LLM reasoning, including assessment criteria and data sampling methods, and demonstrates its effectiveness on physics problems using multiple backbone LLMs.
This article details the technical architecture and training pipeline of IBM's Granite 4.1 LLMs, covering pre-training, SFT, and RL stages. It highlights that the 8B dense model outperforms larger MoE counterparts and notes the release under Apache 2.0 license.
Researchers propose SPS (Steering Probability Squeezing), a training paradigm combining reinforcement learning with inverse reinforcement learning to address probability squeezing in LLM reasoning training, where probability mass concentrates too narrowly on high-reward trajectories, limiting exploration and multi-sample performance (Pass@k). Experiments on five reasoning benchmarks demonstrate improved exploration and Pass@k metrics.