Instructions to use Codeseys/composer-replication-framework with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Codeseys/composer-replication-framework with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Codeseys/composer-replication-framework", dtype="auto") - Notebooks
- Google Colab
- Kaggle
verl_path/ — STATUS: design-only
Status (Wave 18, 2026-05-26): This directory contains a design sketch of the VeRL adapter, not a tested implementation. Validate via Spike 002b (PRIME-RL/VeRL run on real GPU) before relying on any code in here.
Wave 7's vision-validation audit explicitly called out: "the framework claims integration with TRL + VeRL but the only tested path is TRL. verl_path/ should be marked design-only until validated end-to-end." This README closes that audit finding.
What this is
composer_adv.py and composer_config.yaml are reference implementations
of the same compose_loss composition contract as trl_path/, written
against VeRL's algorithm-library surface (advantage estimator + custom
loss hook). They were authored from primary-source reading of
volcengine/verl at the time of Wave 6 and are kept as a design
target — they document HOW we'd wire a 3-channel composer loss into
VeRL's actor/critic update — but they have NOT been run end-to-end
against a real model under VeRL's runtime.
What this is NOT
- Not pip-tested (VeRL has heavy transitive deps including Ray; we haven't paid the cost to install them in the test venv).
- Not import-tested (no
from spikes.005-integrated-trainer-skeleton.verl_path import ...test exists). - Not a parity oracle. The
trl_path/adapter is the production-tested path; theverl_path/files document the proposed VeRL equivalent but their numerical equivalence is unverified.
What to do before relying on it
- Install VeRL + Ray in a fresh venv:
pip install volcengine-verl ray - Stand up a VeRL trainer using
composer_config.yamlagainst a small model (Qwen2.5-0.5B-Instruct works for CPU smoke). - Verify
composer_adv.py:compose_advantage_with_lossproduces the same (lm_ce, sdpo_jsd, trace_replay_dpo) decomposition astrl_path/composer_trainer.py:_compute_losson identical inputs. - If parity holds: promote this README to
STATUS: testedand add the parity test tocomposer_replication/recipes/verl/tests/. - If parity fails: file a follow-up wave to fix the mismatched code path before any user-facing claim that VeRL is supported.
Cross-references
docs/research/RL_FRAMEWORKS_LANDSCAPE.md— landscape audit that fed the design (see "Realised in v0.1" callout for the post-Wave-14b PRIME-RL recipe; the VeRL design here is older).docs/VISION_VALIDATION.md§ 7, Objection 3 — Wave 7 audit identifying this gap.composer_replication/recipes/prime_rl/— the actually-tested third-RL-framework recipe (PRIME-RL, with shadow-parity tests against upstreamdefault_loss_fn). VeRL would land alongside it once validated.