Large reasoning models achieve strong problem-solving performance, but often generate excessively long chain-of-thought outputs, which substantially increase inference cost and limit practical deployment. Existing reasoning compression methods reduce length only partially and often suffer from unstable optimization or accuracy degradation. We trace this limitation to an underlying trajectory-level objective conflict: when the compression objective and the accuracy objective are optimized jointly, compression favors short-and-correct trajectories while accuracy optimization continues to preserve long-but-correct ones that remain useful on hard problems. This conflict creates opposing gradients on the same behaviors and can trap training in a local trade-off where further compression harms accuracy while accuracy recovery reintroduces verbose reasoning. At the model-decision level, we refer to the resulting failure mode as entropy conflict: the policy fails to fully collapse onto concise trajectories because accuracy-oriented updates keep restoring broader support over long-correct reasoning paths. To address this issue, we propose an entropy-guided training framework. Instead of forcing a mixed objective to follow an unstable path toward the final efficient-output set, we first contract the policy to a short-output set and then expand from this compact regime to recover accuracy and robustness. Experiments on multiple mathematical reasoning benchmarks show that our method achieves performance comparable to the original model using only about 25% of its reasoning tokens.

The goal of multilingual modelling is to generate multilingual text representations for various downstream tasks in different languages. However, some state-of-the-art pre-trained multilingual models perform poorly on many low-resource languages due to the lack of representation space and model capacity. To alleviate this issue, we propose a Multilingual model Enhanced with Visual Text Representations (MEVTR), which complements textual representations and extends the multilingual representation space with visual text representations. First, the visual encoder focuses on the glyphs and structure of the text to obtain visual text representations, and the textual encoder obtains textual representations. Then, multilingual representations are enhanced by aligning and fusing visual text representations and textual representations. Moreover, we propose similarity constraint, a self-supervised task to prompt the visual encoder to focus on more additional information. Prefix alignment and multi-head bilinear module are designed to acquire an improved integration effect of visual text representations and textual representations. Experimental results indicate that MEVTR benefits from visual text representations and achieves significant performance gains in downstream tasks. In particular, in the zero-shot cross-lingual transfer task, MEVTR achieves results that outperform the state-of-the-art adapter-based framework without the target language adapter.

In linguistics, all languages can be considered as symbolic systems, with each language relying on symbolic processes to associate specific symbols with meanings. In the same language, there is a fixed correspondence between linguistic symbol and meaning. In different languages, universal meanings follow varying rules of symbolization in one-to-one correspondence with symbols. Most work overlooks the properties of languages as symbol systems. In this paper, we shift the focus to the symbolic properties and introduce MTLS: a pre-training method to improve the multilingual capability of models by Making Texts into Linguistic Symbols. Initially, we replace the vocabulary in pre-trained language models by mapping relations between linguistic symbols and semantics. Subsequently, universal semantics within the symbolic system serve as bridges, linking symbols from different languages to the embedding space of the model, thereby enabling the model to process linguistic symbols. To evaluate the effectiveness of MTLS, we conducted experiments on multilingual tasks using BERT and RoBERTa, respectively, as the backbone. The results indicate that despite having just over 12,000 pieces of English data in pre-training, the improvement that MTLS brings to multilingual capabilities is remarkably significant.