Recent advances, such as DeepSeek R1-Zero, highlight the effectiveness of incentive training, a reinforcement learning paradigm that computes rewards solely based on the final answer part of a language model's output, thereby encouraging the generation of intermediate reasoning steps.However, these methods fundamentally rely on external verifiers, which limits their applicability to domains like mathematics and coding, where such verifiers are readily available.Although reward models can serve as verifiers, they require high-quality annotated data and are costly to train.In this work, we propose NOVER, NO-VERifier Reinforcement Learning, a general reinforcement learning framework that requires only standard supervised fine-tuning data with no need for an external verifier.NOVER enables incentive training across a wide range of text-to-text tasks and outperforms the model of the same size distilled from large reasoning models such as DeepSeek R1 671B by 7.7%.Moreover, the flexibility of NOVER enables new possibilities for optimizing large language models, such as inverse incentive training.

When serving a single base LLM with several different LoRA adapters simultaneously, the adapters cannot simply be merged with the base model’s weights as the adapter swapping would create overhead and requests using different adapters could not be batched. Rather, the LoRA computations have to be separated from the base LLM computations, and in a multi-device setup the LoRA adapters can be sharded in a way that is well aligned with the base model’s tensor parallel execution, as proposed in S-LoRA. However, the S-LoRA sharding strategy encounters some communication overhead, which may be small in theory, but can be large in practice. In this paper, we propose to constrain certain LoRA factors to be block-diagonal, which allows for an alternative way of sharding LoRA adapters that does not require any additional communication for the LoRA computations. We demonstrate in extensive experiments that our block-diagonal LoRA approach is similarly parameter efficient as standard LoRA (i.e., for a similar number of parameters it achieves similar downstream performance) and that it leads to significant end-to-end speed-up over S-LoRA. For example, when serving on eight A100 GPUs, we observe up to 1.79x (1.23x) end-to-end speed-up with 0.87x (1.74x) the number of adapter parameters for Llama-3.1-70B, and up to 1.63x (1.3x) end-to-end speed-up with 0.86x (1.73x) the number of adapter parameters for Llama-3.1-8B.

While Large language models (LLMs) have the capability to iteratively reflect on their own outputs, recent studies have observed their struggles with knowledge-rich problems without access to external resources.In addition to the inefficiency of LLMs in self-assessment, we also observe that LLMs struggle to revisit their predictions despite receiving explicit negative feedback.Therefore, We propose Mirror, a Multiple-perspective self-reflection method for knowledge-rich reasoning, to avoid getting stuck at a particular reflection iteration.Mirror enables LLMs to reflect from multipleperspective clues, achieved through a heuristic interaction between a Navigator and a Reasoner.It guides agents toward diverse yet plausibly reliable reasoning trajectory without access to ground truth by encouraging (1) diversity of directions generated by Navigator and (2) agreement among strategically induced perturbations in responses generated by the Reasoner.The experiments on five reasoning datasets demonstrate that Mirror's superiority over several contemporary self-reflection approaches.Additionally, the ablation study studies clearly indicate that our strategies alleviate the aforementioned challenges.The code is released at https://github.com/hanqi-qi/Mirror.git.

Document-level multi-event extraction aims to extract the structural information from a given document automatically.Most recent approaches usually involve two steps: (1) modeling entity interactions; (2) decoding entity interactions into events.However, such approaches ignore a global view of inter-dependency of multiple events.Moreover, an event is decoded by iteratively merging its related entities as arguments, which might suffer from error propagation and is computationally inefficient.In this paper, we propose an alternative approach for document-level multi-event extraction with event proxy nodes and Hausdorff distance minimization.The event proxy nodes, representing pseudo-events, are able to build connections with other event proxy nodes, essentially capturing global information.The Hausdorff distance makes it possible to compare the similarity between the set of predicted events and the set of ground-truth events.By directly minimizing Hausdorff distance, the model is trained towards the global optimum directly, which improves performance and reduces training time.Experimental results show that our model outperforms previous state-of-the-art method in F1-score on two datasets with only a fraction of training time. 1 1 Code is available at https://github.com/xnyuwg/procnet... [5] Shenkai Petrochemical Co., Ltd. received the receipt from the company's shareholder, Yexiang Investment Management Co., Ltd. on the evening of November 15, 2016 Regarding the notice of the shares being frozen.... [8] On November 14, 2016, Yexiang Investment received the Notice of Litigation Preservation from the People's Court of Binjiang District, and granted a total of 47,577,481 shares held by Yexiang Investment will be frozen, and the freezing period is from October 31, 2016 to October 30, 2019 ... [10] Yexiang Investment is … holding 47,577,481 shares of the company, accounting for 13.07% of the company's total share capital.... [

Table of contents (ToC) extraction centres on structuring documents in a hierarchical manner.In this paper, we propose a new dataset, ESGDoc, comprising 1,093 ESG annual reports from 563 companies spanning from 2001 to 2022.These reports pose significant challenges due to their diverse structures and extensive length.To address these challenges, we propose a new framework for Toc extraction, consisting of three steps: (1) Constructing an initial tree of text blocks based on reading order and font sizes; (2) Modelling each tree node (or text block) independently by considering its contextual information captured in node-centric subtree; (3) Modifying the original tree by taking appropriate action on each tree node (Keep, Delete, or Move).This construction-modellingmodification (CMM) process offers several benefits.It eliminates the need for pairwise modelling of section headings as in previous approaches, making document segmentation practically feasible.By incorporating structured information, each section heading can leverage both local and long-distance context relevant to itself.Experimental results show that our approach outperforms the previous state-of-theart baseline with a fraction of running time.Our framework proves its scalability by effectively handling documents of any length.1