title: Reasoning Path Compression: Compressing Generation Trajectories for
    Efficient LLM Reasoning
summary: Recent reasoning-focused language models achieve high accuracy by generating
  lengthy intermediate reasoning paths before producing final answers. While this
  approach is effective in solving problems that require logical thinking, long
  reasoning paths significantly increase memory usage and throughput of token
  generation, limiting the practical deployment of such models. We propose
  Reasoning Path Compression (RPC), a training-free method that accelerates
  inference by leveraging the semantic sparsity of reasoning paths. RPC
  periodically compresses the KV cache by retaining KV cache that receive high
  importance score, which are computed using a selector window composed of
  recently generated queries. Experiments show that RPC improves generation
  throughput of QwQ-32B by up to 1.60times compared to the inference with full
  KV cache, with an accuracy drop of 1.2% on the AIME 2024 benchmark. Our
  findings demonstrate that semantic sparsity in reasoning traces can be
  effectively exploited for compression, offering a practical path toward
  efficient deployment of reasoning LLMs. Our code is available at
  https://github.com/jiwonsong-dev/ReasoningPathCompression.
paper_authors._id: 682d2dee396c1e613e9fcbe5
paper_authors.name: Jiwon Song
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-21T08:41:15.525Z
paper_authors._id: 662672eaebdfec5cfdf1d034
paper_authors.avatarUrl: /avatars/61bc7add693c555e29ad3c1112215684.svg
paper_authors.fullname: Jiwon Song
paper_authors.type: user
paper_authors.user: jiwonsong
paper_authors._id: 682d2dee396c1e613e9fcbe6
paper_authors.name: Dongwon Jo
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-21T08:41:12.000Z
paper_authors._id: 639ffbc6beb95d698de9640d
paper_authors.avatarUrl: /avatars/7ef1aaadd5b378d00e17dc548e42cb7e.svg
paper_authors.fullname: Dongwon Jo
paper_authors.type: user
paper_authors.user: dongwonjo
paper_authors._id: 682d2dee396c1e613e9fcbe7
paper_authors.name: Yulhwa Kim
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-21T10:14:05.272Z
paper_authors._id: 6566ddb96af53c602f80b1e2
paper_authors.avatarUrl: /avatars/403c8e486115920e50867b6462ddfd99.svg
paper_authors.fullname: Yulhwa Kim
paper_authors.type: user
paper_authors.user: YulhwaKim
paper_authors._id: 682d2dee396c1e613e9fcbe8
paper_authors.name: Jae-Joon Kim
paper_title: Reasoning Path Compression: Compressing Generation Trajectories for
    Efficient LLM Reasoning
paper_summary: Recent reasoning-focused language models achieve high accuracy by generating
  lengthy intermediate reasoning paths before producing final answers. While this
  approach is effective in solving problems that require logical thinking, long
  reasoning paths significantly increase memory usage and throughput of token
  generation, limiting the practical deployment of such models. We propose
  Reasoning Path Compression (RPC), a training-free method that accelerates
  inference by leveraging the semantic sparsity of reasoning paths. RPC
  periodically compresses the KV cache by retaining KV cache that receive high
  importance score, which are computed using a selector window composed of
  recently generated queries. Experiments show that RPC improves generation
  throughput of QwQ-32B by up to 1.60times compared to the inference with full
  KV cache, with an accuracy drop of 1.2% on the AIME 2024 benchmark. Our
  findings demonstrate that semantic sparsity in reasoning traces can be
  effectively exploited for compression, offering a practical path toward
  efficient deployment of reasoning LLMs. Our code is available at
  https://github.com/jiwonsong-dev/ReasoningPathCompression.
paper_ai_summary: Reasoning Path Compression improves inference throughput of reasoning LLMs by exploiting semantic sparsity in reasoning paths without significantly reducing accuracy.
paper_ai_keywords: reasoning-focused language models intermediate reasoning paths memory usage token generation KV cache semantic sparsity selector window QwQ-32B AIME 2024 benchmark