Reinforcement Learning
Transformers
English
post-training
distillation
agentic-coding
composer-2.5
cursor
kimi-k2
grpo
dapo
diloco
openenv
trl
verl
research
methodology
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; the `verl_path/` files document the proposed VeRL equivalent | |
| but their numerical equivalence is unverified. | |
| ## What to do before relying on it | |
| 1. Install VeRL + Ray in a fresh venv: `pip install volcengine-verl ray` | |
| 2. Stand up a VeRL trainer using `composer_config.yaml` against a small | |
| model (Qwen2.5-0.5B-Instruct works for CPU smoke). | |
| 3. Verify `composer_adv.py:compose_advantage_with_loss` produces the | |
| same (lm_ce, sdpo_jsd, trace_replay_dpo) decomposition as | |
| `trl_path/composer_trainer.py:_compute_loss` on identical inputs. | |
| 4. If parity holds: promote this README to `STATUS: tested` and add | |
| the parity test to `composer_replication/recipes/verl/tests/`. | |
| 5. 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 | |
| upstream `default_loss_fn`). VeRL would land alongside it once | |
| validated. | |