Group Relative Policy Optimization (GRPO) is an attractive reinforcement learning paradigm for Large Reasoning Models because its groupwise relative advantages eliminate the need for a value network, but its efficiency is severely limited by zero-variance queries whose rollouts are uniformly correct or uniformly incorrect and therefore consume expensive Chain-of-Thought generation without providing useful learning signals. Existing remedies typically rely on offline difficulty labels or external capability trackers, which either fail to follow the model’s rapidly evolving competence or compromise the end-to-end simplicity of GRPO. We propose Active-GRPO, a prompt-free extension that equips the policy with intrinsic fuzzy metacognition via a lightweight confidence head attached to the final prefill representation. The head predicts ordinal confidence bins and is jointly calibrated against empirical rollout outcomes through a dual-objective optimization framework. It further enables a multi-criterion voting mechanism that filters uninformative queries before rollout generation, thereby concentrating compute on samples near the learning boundary with negligible overhead and without perturbing the reasoning distribution. Experiments on mathematical and logical reasoning benchmarks show that Active-GRPO reduces training computation while improving both reasoning accuracy and intrinsic calibration quality.

Large Language Models (LLMs) exhibit strong general reasoning but struggle in molecular science due to the lack of explicit priors required for precise chemical reasoning. Current solutions inject priors into parameters, but this static coupling hinders rapid knowledge updates and often compromises the model’s general capabilities. To address this, we introduce REAP, a training-free framework that equips LLMs with an external knowledge base, enabling them to reason over retrieved chemical priors dynamically. REAP implements a structured reasoning pipeline that autonomously selects relevant priors from our constructed atom-level knowledge base, retrieves analogue exemplars, and synthesizes these information to guide the LLM’s decision-making. This architecture ensures interpretability and adaptability while preserving the LLM’s intrinsic general intelligence. Experiments show that REAP outperforms current reasoning methods and rivals state-of-the-art training-based models, demonstrating the effectiveness of our framework.

Reference-conditioned image generation increasingly relies on visual histories that rarely speak with one voice: one example pins the subject, another fixes a layout, a third only suggests a palette. Current image-conditioned diffusion methods encode these references once, average them into a single vector, and inject it at every denoising step, so conflicting cues collide and the output is faithful to none. We propose the Hierarchical Temporal Guidance Framework (HTGF), a training-free pipeline that reframes multi-reference conditioning as a *temporal routing* problem. A semantic decomposer reads each reference and emits a soft routing distribution over three axes (Subject, Structure, Detail), and a closed-form SNR-sensitivity argument places each axis in the denoising window where it has the most leverage. A short manifold-aware corrector then smooths the trajectory at the stage boundaries where the active condition changes. HTGF adds no training and no architectural change to the diffusion backbone. On three datasets it outperforms strong VLM- and diffusion-based baselines, gaining +11.7 CIS over LaVIT on Movie Poster and FID 38.87 vs. 50.12 for the best baseline under noisy histories, and degrading gracefully down to the zero-reference limit.

On-policy distillation (OPD) provides dense token-level supervision by asking a teacher to score student-generated rollouts. However, when the student drifts into an unrecoverable prefix, the teacher may locally agree with the degraded state, producing low reverse KL but little corrective training signal. We identify this persistent regime as a low-KL agreement trap. Further analyses show that tokens during and after such traps produce less useful supervision signals. We propose KAT (KL Agreement Trap Termination), an online OPD termination rule that detects persistent low-KL agreement with a dynamic training-adaptive threshold. By filtering weak supervision from degenerate agreement, KAT improves avg@k accuracy by 2.66% and pass@k by 3.43% across four mathematical benchmarks, while reducing average rollout length by 59.73%.

Representation Fine-tuning (ReFT), a recently proposed parameter-efficient fine-tuning (PeFT) method, significantly improves parameter efficiency by modifying the representation space alone. However, directly applying ReFT, which alters a fixed number of representations at the beginning and end positions of each layer, results in suboptimal performance for two reasons. (i) The impact of these fixed-position representations on the output is uncertain; (ii) As the sequence length increases, fine-tuning a fixed number of representations may have diminishing effects on the final results. Based on our observations that punctuation plays a crucial role in integrating representations from preceding layers and modulating those of subsequent layers, we introduce Punctuation-steered Representation Fine-tuning (PuReFT), a straightforward yet powerful approach that additionally fine-tunes punctuation representations to achieve performance improvements. Extensive evaluations on common-sense, arithmetic, and code datasets demonstrate the effectiveness and versatility of PuReFT. Furthermore, our analysis of its training speed and memory overhead confirms its greater ease of use and efficiency.

Structured pruning offers a hardware-friendly approach for efficient LLM inference. Early static methods determine fixed subnetworks through offline calibration, suffering from performance degradation and calibration sensitivity. Recent methods explore input-adaptive pruning by selecting a subset of tokens as probes to estimate hidden activations for online pruning decisions.However, existing probe selection strategies fail to identify outlier-triggering tokens, and uniform layer-wise sparsity misaligns with heterogeneous outlier distributions, leading to critical channels being incorrectly pruned. Therefore, we propose OCP (Outlier-Centric Probing for structured pruning), a principled framework that prioritizes capturing outlier-triggering tokens rather than reconstructing full hidden distributions. Specifically, OCP includes three key components: (1) sensitivity-weighted probing for FFN layers that identifies outlier patterns via precomputed weight aggregations, (2) attention-accumulated probing that leverages preceding attention matrices to identify salient tokens, and (3) online adaptive sparsity allocation that dynamically adjusts layer-wise pruning based on history-guided outlier statistics. Extensive experiments on LLaMA2, LLaMA3, and OPT demonstrate that OCP consistently outperforms state-of-the-art methods across benchmarks, achieving up to 25% perplexity reduction at 1.6× speedup.