Unlocking Fine-Grained Translation Quality Estimation in LRMs through Synergistically Evolving Implicit and Explicit Reasoning

Large Reasoning Models (LRMs) still struggle with fine-grained translation quality estimation (QE), even with long reasoning chains. We argue that LRMs already possess strong multilingual capabilities, while the core challenge stems from the intrinsic difficulty of learning the fine-grained QE task. In this paper, we propose RIEQE (Reasoning both Implicitly and Explicitly for QE), a simple two-stage training framework that enables the co-evolution of implicit (layer-wise) and explicit (token-wise) reasoning capabilities. To make implicit reasoning feasible, we first decompose the complex QE task into straightforward subtasks. Based on this, our two-stage approach applies: (1) NonThinking-SFT, Supervised Fine-Tuning (SFT) without reasoning chains to directly boost the model's implicit reasoning tendency and capability; and (2) Thinking-RLVR, standard Reinforcement Learning with Verifiable Reward (RLVR) to subsequently strengthen explicit reasoning. Results demonstrate that implicit and explicit reasoning synergistically co-evolve under our framework. On the WMT test sets, RIEQE based on Qwen3-4B-Thinking-2507 surpasses all baselines in explicit reasoning performance, while its implicit reasoning capability is also comparable to the best current encoder-based models. We further provide evidence for the synergistic collaboration between implicit and explicit reasoning, showing how they mutually benefit each other.

Internal Bias in Reasoning Models leads to Overthinking

While current reasoning models possess strong exploratory capabilities, they are often criticized for overthinking due to redundant and unnecessary reflections. In this work, we reveal for the first time that overthinking in reasoning models may stem from their internal bias towards input texts. Upon encountering a reasoning problem, the model immediately forms a preliminary guess about the answer, which we term as an internal bias since it is not derived through actual reasoning. When this guess conflicts with its reasoning result, the model tends to engage in reflection, leading to the waste of computational resources. Through further interpretability experiments, we find that this behavior is largely driven by the model's excessive attention to the input section, which amplifies the influence of internal bias on its decision-making process. Additionally, by masking out the original input section, the affect of internal bias can be effectively alleviated and the reasoning length could be reduced by 31%-53% across different complex reasoning tasks. Notably, in most cases, this approach also leads to improvements in accuracy. These findings demonstrate a causal relationship between internal bias and overthinking.