arXiv (All)
open access
Unlocking Structured Pruning for Reasoning Language Models via Attention-Preserving MLP Trimming
Structured pruning reduces LLM inference costs by removing model components to produce smaller dense models. However, existing methods can cause reasoning performance to collapse on long-form reasoning tasks such as AIME. We propose \textit{LayerTrim}, a training-free structured pruning method that
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Look Back, Think Ahead: Visual Memory on Demand for Efficient Multimodal Reasoning#1249484
- Advantageous Parameter Expansion Training Makes Better Large Language Models#670342
- EchoCoT: Extracting Hidden Chain-of-Thought from Large Reasoning Models#471181
- TOPS: First-Principles Visual Token Pruning via Constructing Token Optimal Preservation Sets for Efficient MLLM Inference#1063955
- Escaping Reasoning Basin Collapse with History-Biased Search#1306332
Record · ID 1344944
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive