Large Reasoning Models(LRMs) have achieved significant breakthroughs over prior large language models (LLMs), but they also entail greater potential safety risks. Existing alignment methods often remain at a shallow level of protection, making them insufficient to address deeper risks and strategic attacks in complex reasoning processes. To bridge this gap, we move beyond the conventional paradigm that treats safety alignment merely as a preventive measure to reduce harmful outputs. Drawing inspiration from human-like introspection and self-correction, we propose Self-Reflection, a technique that introduces a special Self-Reflection token, enabling LRMs to perform Self-Reflection during generation and recover from harmful outputs. Our approach integrates seamlessly into standard post-training paradigms , further enhancing both helpfulness and safety. The experimental results demonstrate that models trained with Self-Reflection not only consistently outperform the baseline in terms of safety (reducing the HCR from 13.8% to 4.1%, nearly a threefold improvement over mainstream approaches), but also achieve substantial advantages in both helpfulness and the safety–helpfulness balance. More importantly, under evaluations involving various adversarial attacks, including a specially designed adaptive attack, the Self-Reflection mechanism significantly enhances model safety without targeted adversarial training.Notice: This paper contains harmful content.

Reasoning segmentation requires models to ground complex, implicit textual queries into precise pixel-level masks. Existing approaches rely on a single segmentation token \<SEG\>, whose hidden state implicitly encodes both semantic reasoning and spatial localization, limiting the model’s ability to explicitly disentangle *what to segment* from *where to segment*. We introduce AnchorSeg, which reformulates reasoning segmentation as a structured conditional generation process over image tokens, conditioned on language grounded query banks. Instead of compressing all semantic reasoning and spatial localization into a single embedding, AnchorSeg constructs an ordered sequence of query banks: latent reasoning tokens that capture intermediate semantic states, and a segmentation anchor token that provides explicit spatial grounding. We model spatial conditioning as a factorized distribution over image tokens, where the anchor query determines localization signals while contextual queries provide semantic modulation. To bridge token-level predictions and pixel-level supervision, we propose Token–Mask Cycle Consistency (TMCC), a bidirectional training objective that enforces alignment across resolutions. By explicitly decoupling spatial grounding from semantic reasoning through structured language grounded query banks, AnchorSeg achieves state-of-the-art results on ReasonSeg test set (67.7% gIoU and 68.1% cIoU). All code and models are publicly available at https://github.com/rui-qian/AnchorSeg.

Reinforcement learning (RL) has emerged as an effective approach for enhancing the reasoning capabilities of large language models (LLMs), especially in scenarios where supervised fine-tuning (SFT) falls short due to limited chain-of-thought (CoT) data. Among RL-based post-training methods, group relative advantage estimation, as exemplified by Group Relative Policy Optimization (GRPO), has attracted considerable attention for eliminating the dependency on the value model, thereby simplifying training compared to traditional approaches like Proximal Policy Optimization (PPO). However, existing group relative advantage estimation method still suffers from training inefficiencies, particularly when the estimated advantage approaches zero. To address this limitation, we propose Advantage-Augmented Policy Optimization (AAPO), a novel RL algorithm that optimizes the cross-entropy (CE) loss using advantages enhanced through a margin-based estimation scheme. This approach effectively mitigates the inefficiencies associated with group relative advantage estimation. Experimental results on multiple mathematical reasoning benchmarks and model series demonstrate the superior performance of AAPO. Code is available at https://github.com/JianxXiong/AAPO.