Memp: Exploring Agent Procedural Memory
Large Language Models (LLMs) based agents excel at diverse tasks, yet they
suffer from brittle procedural memory that is manually engineered or entangled
in static parameters. In this work, we investigate strategies to endow agents
with a learnable, updatable, and lifelong procedural memory. We propose Memp
that distills past agent trajectories into both fine-grained, step-by-step
instructions and higher-level, script-like abstractions, and explore the impact
of different strategies for Build, Retrieval, and Update of procedural memory.
Coupled with a dynamic regimen that continuously updates, corrects, and
deprecates its contents, this repository evolves in lockstep with new
experience. Empirical evaluation on TravelPlanner and ALFWorld shows that as
the memory repository is refined, agents achieve steadily higher success rates
and greater efficiency on analogous tasks. Moreover, procedural memory built
from a stronger model retains its value: migrating the procedural memory to a
weaker model yields substantial performance gains.
6899b66999477d568629a468
Runnan Fang
6899b66999477d568629a469
Yuan Liang
6899b66999477d568629a46a
Xiaobin Wang
6899b66999477d568629a46b
Jialong Wu
6899b66999477d568629a46c
Shuofei Qiao
6899b66999477d568629a46d
Pengjun Xie
6899b66999477d568629a46e
Fei Huang
6899b66999477d568629a46f
Huajun Chen
6899b66999477d568629a470
Ningyu Zhang
claimed_verified
2025-08-13T07:22:49.125Z
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/avatars/e0fccbb2577d76088e09f054c35cffbc.svg
Ningyu Zhang
user
Ningyu
Memp: Exploring Agent Procedural Memory
Large Language Models (LLMs) based agents excel at diverse tasks, yet they
suffer from brittle procedural memory that is manually engineered or entangled
in static parameters. In this work, we investigate strategies to endow agents
with a learnable, updatable, and lifelong procedural memory. We propose Memp
that distills past agent trajectories into both fine-grained, step-by-step
instructions and higher-level, script-like abstractions, and explore the impact
of different strategies for Build, Retrieval, and Update of procedural memory.
Coupled with a dynamic regimen that continuously updates, corrects, and
deprecates its contents, this repository evolves in lockstep with new
experience. Empirical evaluation on TravelPlanner and ALFWorld shows that as
the memory repository is refined, agents achieve steadily higher success rates
and greater efficiency on analogous tasks. Moreover, procedural memory built
from a stronger model retains its value: migrating the procedural memory to a
weaker model yields substantial performance gains.
Agents equipped with a learnable, updatable procedural memory system, Memp, achieve improved performance and efficiency across tasks by distilling past experiences into detailed instructions and higher-level abstractions.
Large Language Models procedural memory Memp agent trajectories fine-grained instructions script-like abstractions Build Retrieval Update dynamic regimen TravelPlanner ALFWorld performance gains