Soft Token Alignment for Cross-Lingual Reasoning

• URL: http://arxiv.org/abs/2606.26466v1
• Date: Thu, 25 Jun 2026 00:01:58 GMT
• Title: Soft Token Alignment for Cross-Lingual Reasoning
• Abstract summary: We propose SOLAR, an auxiliary objective for supervised fine-tuning that aligns soft-token representations across languages.<n>We then align each non-English soft-token summary to its English counterpart in the shared embedding space.<n> SOLAR improves accuracy by up to +17.7 points over the base model and +3.8 over standard supervised fine-tuning.
• Score: 31.882767320907572
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multilingual large language models often produce inconsistent reasoning and answers for semantically equivalent prompts in different languages. Prior work suggests that intermediate representations can be relatively language-agnostic, but generation becomes increasingly language-specific as models commit to discrete output tokens. This is problematic because language-specific lexical choices can cause semantically equivalent reasoning paths to diverge across languages. These divergences motivate searching for a cross-lingual alignment signal that is less tied to any single vocabulary item or script. We propose SOLAR, an auxiliary objective for supervised fine-tuning that aligns soft-token representations across languages, using English as a pivot. Soft tokens are probability-weighted mixtures over the vocabulary embeddings, yielding continuous representations that can aggregate information from semantically related tokens across languages. We then align each non-English soft-token summary to its English counterpart in the shared embedding space. Across four multilingual reasoning benchmarks, SOLAR improves accuracy by up to +17.7 points over the base model and +3.8 over standard supervised fine-tuning, with the largest gains on low-resource languages. SOLAR also strengthens final-layer cross-lingual similarity and substantially reduces language-cluster separability, suggesting that aligning soft-token representations helps preserve shared semantic structure during multilingual reasoning.

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