Despite their strong reasoning capabilities and extensive world knowledge, Large Language Models (LLMs) frequently generate plans that violate task constraints, undermining their reliability in real-world applications. This deficiency arises from a lack of systematic mechanisms to incorporate constraint information during the generation process. While existing approaches attempt to mitigate this by relying on external tools or task decomposition, they fail to enhance the model’s intrinsic constraint awareness. To address this, we propose Constraint-Aware Reinforcement Learning (CARL), a novel RL framework designed to strengthen LLMs’ intrinsic focus on constraints. CARL introduces a constraint-aware reward by comparing the model’s output distributions under constrained and unconstrained inputs, encouraging constraint focus and penalizing neglect.Compatible with various RL frameworks and requiring no external solvers or top models, CARL enables scalable, end-to-end constraint-aware planning. Extensive experiments on BlocksWorld, TravelPlanner, and T-Eval demonstrate that CARL significantly outperforms standard Reinforcement Fine-Tuning (RFT) baselines and state-of-the-art reasoning models, exhibiting a markedly increased focus on constraints.

Existing curriculum learning methods for reasoning reinforcement learning often treat problem difficulty as a static property of the problem. We argue that difficulty is instead policy-dependent: problems beyond the current policy provide sparse learning signals, while problems already solved by greedy decoding may still yield informative stochastic trajectories during RL. We propose PAD-Curriculum, a policy-adaptive curriculum framework for GRPO that estimates the student’s evolving solvable frontier from student-generated trajectories. For currently solved problems, PAD-Curriculum ranks examples by a normalized Scorer-to-Student KL discrepancy computed against a frozen stronger scorer; unsolved problems are deferred and periodically re-evaluated as the policy improves. Experiments on GSM8K, MATH, and NuminaMath-CoT with Llama-3.1 and Qwen3 models show consistent gains over standard GRPO and curriculum/distillation baselines, with particularly strong improvements on challenging mathematical reasoning benchmarks. Analyses further show that adaptive discrepancy estimation is more effective than static scorer-based difficulty.

Large language models (LLMs) frequently produce confident yet factually incorrect responses when user inputs contain misleading premises, a phenomenon we attribute to fact perturbations in the input. Existing approaches to hallucination mitigation typically assume reliable user inputs, overlooking how such factual errors can actively mislead model reasoning. To address this vulnerability, we propose DEDUCE, a three-stage framework that transforms LLMs from passive responders into proactive error correctors. DEDUCE operates in three stages: (1) detect errors through fine-grained fact extraction and verification; (2) devise correction strategies via multi perspective deliberation; and (3) correct misconceptions while delivering reliable answers. We also present MisFactQA, a dataset containing factual errors of varying degrees, and propose new metrics for evaluating model robustness. Experiments on TruthfulQA, FalseQA, and our MisFactQA benchmark demonstrate that DEDUCE significantly improves both accuracy and error correction capability. Consistent gains across Qwen, LLaMA, and Gemma families confirm its effectiveness and scalability.

Large Language Models enhanced with Retrieval Augmented Generation show strong potential in knowledge intensive tasks. However, they often encounter knowledge conflicts, where retrieved information contradicts the model’s internal knowledge or exhibits internal inconsistencies. Existing methods treat this as a simplistic binary choice, forcing models to blindly trust external contexts or rigidly rely on memory, resulting in unreliable predictions that swing between sycophancy and stubbornness. We argue that a more principled approach is to embrace contradictions as opportunities for deeper reasoning. To this end, we introduce Debate-of-Thoughts (DoT), a framework that transforms conflict resolution into an active deliberation process. DoT guides a single model through three phases: 1) hypothesis generation, which forms competing perspectives; 2) internal debate, where the model acts as both a proponent and a critic to stress test each view; and 3) adjudication, where a judge module evaluates arguments based on evidence and logical consistency. We implement DoT via two complementary strategies: inference time prompt chaining and supervised fine tuning. Experiments across multiple conflict benchmarks show that DoT consistently outperforms state-of-the-art methods, while generating transparent debate transcripts that explain its decisions. By improving both accuracy and interpretability under knowledge conflicts, DoT establishes a more reliable paradigm for retrieval augmented generation systems.

Large language models (LLMs) exhibit excellent performance in natural language processing (NLP), but remain highly sensitive to the quality of input queries, especially when these queries contain misleading or inaccurate information. Existing methods focus on correcting the output, but they often overlook the potential of improving the ability of LLMs to detect and correct misleading content in the input itself. In this paper, we propose a novel three-stage fine-tuning method that enhances the ability of LLMs to detect and correct misleading information in the input, further improving response accuracy and reducing hallucinations. Specifically, the three stages include (1) training LLMs to identify misleading information, (2) training LLMs to correct the misleading information using built-in or external knowledge, and (3) training LLMs to generate accurate answers based on the corrected queries. To evaluate our method, we conducted experiments on three datasets for the hallucination detection task and the question answering (QA) task, as well as two datasets containing misleading information that we constructed. The experimental results demonstrate that our method significantly improves the accuracy and factuality of LLM responses, while also enhancing the ability to detect hallucinations and reducing the generation of hallucinations in the output, particularly when the query contains misleading information.

Large language models (LLMs) have achieved remarkable advancements in natural language processing. However, the massive scale and computational demands of these models present formidable challenges when considering their practical deployment in resource-constrained environments. While techniques such as chain-of-thought (CoT) distillation have displayed promise in distilling LLMs into small language models (SLMs), there is a risk that distilled SLMs may still inherit flawed reasoning and hallucinations from LLMs. To address these issues, we propose a twofold methodology: First, we introduce a novel method for distilling the self-evaluation capability from LLMs into SLMs, aiming to mitigate the adverse effects of flawed reasoning and hallucinations inherited from LLMs. Second, we advocate for distilling more comprehensive thinking by incorporating multiple distinct CoTs and self-evaluation outputs, to ensure a more thorough and robust knowledge transfer into SLMs. Experiments on three NLP benchmarks demonstrate that our method significantly improves the performance of distilled SLMs, offering a new perspective for developing more effective and efficient SLMs in resource-constrained environments.