Datasets:

Racktic
/

memory-writing-train-tasks

license: mit
pretty_name: Memory Writing Training Task Streams
task_categories:
  - reinforcement-learning
language:
  - en
tags:
  - continual-learning
  - memory
  - agents
  - swe-smith
  - alchemy
configs:
  - config_name: symbolic_alchemy
    data_files: symbolic_alchemy/train_streams.jsonl
  - config_name: codebase_adaptation
    data_files: codebase_adaptation/train_streams.jsonl
  - config_name: codebase_adaptation_tasks
    data_files: codebase_adaptation/tasks.jsonl

Training Task Streams for Memory Writing

This repository contains the training task streams used in the blog post How to Train Memory Writing for Continual Learning. Each row of a train_streams.jsonl file is one stream: an ordered sequence of tasks that the model solves one after another, carrying a memory from each task to the next.

Domain	File	Streams	Tasks per stream
Symbolic Alchemy	`symbolic_alchemy/train_streams.jsonl`	950	10
Codebase Adaptation	`codebase_adaptation/train_streams.jsonl`	382	12

Symbolic Alchemy

Each stream is one prebuilt episode of the level perceptual_mapping_randomized_with_random_bottleneck shipped with dm_alchemy, which contains 1000 episodes with fixed chemistries. The 10 trials of an episode share the same chemistry and form the tasks of the stream, with at most 20 steps per trial.

metadata.episode_index indexes the prebuilt episode set. The prompt field is a placeholder, since the environment state is rebuilt from the episode index.
metadata.max_steps_per_trial is the step budget per task.
metadata.rewrite_granularity is trial, meaning memory is written after every task.
The 20 episodes used for evaluation are excluded. Streams are ordered from easy to hard by the number of edges in the chemistry graph (12, 10, 9, 8, 7), and shuffled within each level.

Codebase Adaptation

Each stream contains 12 bug-fixing tasks: 6 issues from one Python repository (repoA) followed by 6 issues from another (repoB). The issues are synthetic bugs from SWE-smith, drawn from 53 repositories. The streams use 4584 distinct issues, and each issue appears in exactly one stream.

codebase_adaptation/train_streams.jsonl fields (under metadata):

instance_ids: the 12 issues in order. stage_labels gives the repository of each issue and strategies the SWE-smith bug-generation strategy.
difficulty_tier: the composition of each 6-issue segment, with difficulty bucketed by the measured pass rate of the bug-generation strategy. pure_easy (63 streams) has 6 easy issues, easy_mid (138) has 3 easy and 3 medium, and mixed (181) has 2 easy, 2 medium and 2 hard. Within a segment, issues are ordered from easiest to hardest.

codebase_adaptation/tasks.jsonl has one record for each of the 4584 issues:

problem_statement: the issue text shown to the model.
repo, base_commit: the SWE-smith mirror repository and the branch that holds the bug.
image_name: the SWE-smith Docker image of the repository.
FAIL_TO_PASS, PASS_TO_PASS: the tests used for grading. PASS_TO_PASS is restricted to tests in the same files as the FAIL_TO_PASS tests.
test_patch: restores the test files that SWE-smith removes from the bug branch, applied before grading.
test_command, workdir, test_timeout: how the tests are run.
strategy, tier, cluster, n_f2p, n_p2p_local: features used to build the streams.

Sources and licenses

The Codebase Adaptation tasks are derived from SWE-smith (MIT license). The Symbolic Alchemy streams index the prebuilt episodes of dm_alchemy, which are not redistributed here. Please also cite these works if you use the corresponding data.