Reinforcement learning (RL) has emerged as a promising paradigm for training reasoning-oriented models by leveraging rule-based reward signals. However, RL training typically tends to improve single-sample success rates (i.e., Pass@1) while offering limited exploration of diverse reasoning trajectories, which is crucial for multi-sample performance (i.e., Pass@k). Our preliminary analysis reveals that this limitation stems from a fundamental squeezing effect, whereby probability mass is excessively concentrated on a narrow subset of high-reward trajectories, restricting genuine exploration and constraining attainable performance under RL training. To address this issue, in this work, we propose Steering Probability Squeezing (SPS), a training paradigm that interleaves conventional RL with inverse reinforcement learning (IRL). SPS treats on-policy rollouts as demonstrations and employs IRL to explicitly reshape the induced trajectory distribution, thereby enhancing exploration without introducing external supervision. Experiments on five commonly used reasoning benchmarks demonstrate that SPS can enable better exploration and improve Pass@k. Beyond algorithmic contributions, we provide an analysis of RL learning dynamics and identify an empirical upper bound on Pass@k, shedding light on intrinsic exploration limits in RL-based reasoning models. Our findings suggest that alternating between RL and IRL offers an effective pathway toward extending the exploration capacity of reasoning-oriented large language models.

Test-Time Scaling has shown notable efficacy in addressing complex problems through scaling inference compute. However, within Large Audio-Language Models (LALMs), an unintuitive phenomenon exists: post-training models for structured reasoning trajectories results in marginal or even negative gains compared to training for direct answering. To investigate it, we introduce **CAFE**, an evaluation framework designed to quantify audio reasoning errors. Evaluation results reveal that LALMs encounter a critical bottleneck: **Audio Perception Decay**, which compromises reasoning performance. To address it, we propose **MPAR²**, a strategy that encourages a step-by-step reasoning process with rich perceptual awareness. With reinforcement learning, MPAR² improves performance on CAFE from 31.74% to 63.51% and effectively mitigates audio perception decay, concurrently enhancing reasoning ability to achieve a strong 74.59% accuracy on the MMAU benchmark. Further analysis shows that MPAR² optimizes audio attention and enhances reasoning token budget efficiency.

Translating LaTeX documents is challenging because natural-language content is tightly interleaved with executable structures such as commands, equations, environments, and cross-references. Direct translation may therefore corrupt document structure and lead to formatting errors or compilation failures. We present LaTeXTrans, a structure-aware framework that separates semantic translation from executable LaTeX structure through four stages: structure-aware parsing, protected translation, structural validation, and document reconstruction. LaTeXTrans isolates non-translatable structures with protected placeholders, validates structural constraints after translation, and reconstructs the translated content into the original multi-file project structure. We evaluate LaTeXTrans on 100 real-world LaTeX projects across multiple domains and translation directions. Experimental results show consistent improvements in translation quality over direct large language model (LLM) translation while maintaining strong structural reliability. On English–Chinese translation, LaTeXTrans achieves a 97% compilation success rate with only 0.43 formatting errors per document on average. Controlled experiments further show that its structural performance remains stable when previously unseen user-defined command and environment identifiers are introduced.