Reasoning Path Compression: Compressing Generation Trajectories for
  Efficient LLM Reasoning
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.
682d2dee396c1e613e9fcbe5
Jiwon Song
claimed_verified
2025-05-21T08:41:15.525Z
662672eaebdfec5cfdf1d034
/avatars/61bc7add693c555e29ad3c1112215684.svg
Jiwon Song
user
jiwonsong
682d2dee396c1e613e9fcbe6
Dongwon Jo
claimed_verified
2025-05-21T08:41:12.000Z
639ffbc6beb95d698de9640d
/avatars/7ef1aaadd5b378d00e17dc548e42cb7e.svg
Dongwon Jo
user
dongwonjo
682d2dee396c1e613e9fcbe7
Yulhwa Kim
admin_assigned
2025-05-21T10:14:05.272Z
6566ddb96af53c602f80b1e2
/avatars/403c8e486115920e50867b6462ddfd99.svg
Yulhwa Kim
user
YulhwaKim
682d2dee396c1e613e9fcbe8
Jae-Joon Kim
Reasoning Path Compression: Compressing Generation Trajectories for
  Efficient LLM Reasoning
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.
Reasoning Path Compression improves inference throughput of reasoning LLMs by exploiting semantic sparsity in reasoning paths without significantly reducing accuracy.
reasoning-focused language models intermediate reasoning paths memory usage token generation KV cache semantic sparsity selector window QwQ-32B AIME 2024 benchmark