Process Reward Models (PRMs) have emerged as a promising solution to address the reasoning mistakes of large language models (LLMs). However, existing PRMs typically output evaluation scores directly, limiting both learning efficiency and evaluation accuracy. This limitation is further compounded by the scarcity of annotated data. To address these issues, we propose Reasoning-Driven Process Reward Modeling (R-PRM), which activates inherent reasoning to enhance process-level evaluation. First, we leverage stronger LLMs to generate seed data from limited annotations, effectively activating reasoning capabilities and enabling comprehensive step-by-step evaluation. Second, we explore self-improvement of our PRM through preference optimization, without requiring additional annotated data. Third, we introduce inference time scaling to fully harness our model’s reasoning potential. Extensive experiments demonstrate R-PRM’s effectiveness: on ProcessBench and PRMBench, it surpasses strong baselines by 13.9 and 8.5 F1 scores. When applied to guide mathematical reasoning, R-PRM achieves consistent accuracy improvements of over 8.6 points across six challenging datasets. Further analysis reveals that R-PRM exhibits more comprehensive evaluation and robust generalization, indicating its broader potential.

Relational triple extraction is challenging for its difficulty in capturing rich correlations between entities and relations. Existing works suffer from 1) heterogeneous representations of entities and relations, and 2) heterogeneous modeling of entity-entity interactions and entity-relation interactions. Therefore, the rich correlations are not fully exploited by existing works. In this paper, we propose UniRel to address these challenges. Specifically, we unify the representations of entities and relations by jointly encoding them within a concatenated natural language sequence, and unify the modeling of interactions with a proposed Interaction Map, which is built upon the off-the-shelf self-attention mechanism within any Transformer block. With comprehensive experiments on two popular relational triple extraction datasets, we demonstrate that UniRel is more effective and computationally efficient. The source code is available at https://github.com/wtangdev/UniRel.

This report describes our VolcTrans system for the WMT22 shared task on large-scale multilingual machine translation. We participated in the unconstrained track which allows the use of external resources. Our system is a transformer-based multilingual model trained on data from multiple sources including the public training set from the data track, NLLB data provided by Meta AI, self-collected parallel corpora, and pseudo bitext from back-translation. Both bilingual and monolingual texts are cleaned by a series of heuristic rules. On the official test set, our system achieves 17.3 BLEU, 21.9 spBLEU, and 41.9 chrF2++ on average over all language pairs. Averaged inference speed is 11.5 sentences per second using a single Nvidia Tesla V100 GPU.