Representation learning is fundamental to NLP, but building embeddings that work well at different computational budgets is challenging. Matryoshka Representation Learning (MRL) offers a flexible inference paradigm through nested embeddings; however, learning such structures requires explicit coordination of how information is arranged across embedding dimensionality and model depth. In this work, we propose MIPIC (Matryoshka Representation Learning via Self-Distilled Intra-Relational Alignment and Progressive Information Chaining), a unified training framework designed to produce structurally coherent and semantically compact Matryoshka representations. MIPIC promotes cross-dimensional structural consistency through Self-Distilled Intra-Relational Alignment (SIA), which aligns token-level geometric and attention-driven relations between full and truncated representations using top-k CKA self-distillation. Complementarily, it enables depth-wise semantic consolidation via Progressive Information Chaining (PIC), a scaffolded alignment strategy that incrementally transfers mature task semantics from deeper layers into earlier layers. Extensive experiments on STS, NLI, and classification benchmarks (spanning models from TinyBERT to BGEM3, Qwen3) demonstrate that MIPIC yields Matryoshka representations that are highly competitive across all capacities, with significant performance advantages observed under extreme low-dimensional.

Matryoshka Representation Learning (MRL) produces nested embeddings whose prefixes remain semantically meaningful, allowing representations to be truncated at inference time and enabling a single model to operate across multiple dimensional budgets. This paradigm has been adopted in several modern embedding models such as Qwen3-Embedding and OpenAI text-embedding-3. However, distilling MRL teachers into compact students remains largely unexplored, as conventional knowledge distillation methods focus on aligning single-scale embeddings and do not account for the multi-scale structure of Matryoshka representations. We introduce Matryoshka Representation Distillation and propose SUGAR, a new framework that preserves the structural properties of MRL embeddings during knowledge distillation. SUGAR aligns point-wise semantic fidelity, spectral capacity via effective-rank matching, and global subspace geometry through Grassmannian alignment. Our extensive experiments show that SUGAR produces compact students that retain strong performance across multiple embedding dimensions while preserving deployment flexibility.

Knowledge distillation (KD) is crucial for compressing large text embedding models, but faces challenges when teacher and student models use different tokenizers (Cross-Tokenizer KD - CTKD). Vocabulary mismatches impede the transfer of relational knowledge encoded in deep representations, such as hidden states and attention matrices, which are vital for producing high-quality embeddings. Existing CTKD methods often focus on direct output alignment, neglecting this crucial structural information. We propose a novel framework tailored for CTKD embedding model distillation. We first map tokens one-to-one via Minimum Edit Distance (MinED). Then, we distill intra-model relational knowledge by aligning attention matrix patterns using Centered Kernel Alignment, focusing on the top-m most important tokens of the directly mapped tokens. Simultaneously, we align final hidden states via Optimal Transport with Importance-Scored Mass Assignment, which emphasizes semantically important token representations, based on importance scores derived from attention weights. We evaluate distillation from state-of-the-art embedding models (e.g., LLM2Vec, BGE) to a Bert-base-uncased model on embedding-reliant tasks such as text classification, sentence pair classification, and semantic textual similarity. Our proposed framework significantly outperforms existing CTKD baselines. By preserving attention structure and prioritizing key representations, our approach yields smaller, high-fidelity embedding models despite tokenizer differences.