pretty_name: RPMem Training and Benchmark Data
language:
- en
tags:
- rpmem
- parametric-memory
- conversational-memory
configs:
- config_name: compiler_sessions
data_files:
- split: records
path: compiler/sessions/*.parquet
- config_name: compiler_probes
data_files:
- split: records
path: compiler/probes/*.parquet
- config_name: compiler_indices
data_files:
- split: train
path: compiler/indices/train/*.parquet
- split: query_validation
path: compiler/indices/query_validation/*.parquet
- split: validation
path: compiler/indices/validation/*.parquet
RPMem Training and Benchmark Data
Prepared data for RPMem: Learning Recurrent Parametric Memory Across Sessions for LLM Agents. Code and training instructions.
Contents
| Directory | Contents |
|---|---|
compiler/ |
Sessions, probes, reference answers, and split indices (2.9 GB) |
benchmarks/perma/ |
Prepared PERMA data |
benchmarks/personamem_v2/formal_v1/ |
Prepared PersonaMem-v2 data |
benchmarks/prefeval/formal_v1/ |
Prepared PrefEval data |
Total: 4.3 GB. Teacher caches and model weights are not included.
Download
hf download PolarSnowLeopard/RPMem-data --repo-type dataset --local-dir data/rpmem
For a partial download, add --include "compiler/**" or --include "benchmarks/**".
Usage
For compiler training,
use compiler/train.corpus.json with the supplied split indices, not the Hub
viewer's records split. Teacher targets are generated using the code repository.
For benchmark training and evaluation,
use the directories above and skip raw-data preparation. To reproduce the
historical paper runs, select --first-session-rule gate_zero_state
(FIRST_SESSION_RULE=gate_zero_state for the PERMA launcher).
Sources
See SOURCES.md for upstream datasets and their respective terms.