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:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Codeseys/composer-replication-framework", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Wave 21b: skip zero-signal SDPO on empty-recovery error turns + real-trace validation
Found by validating the Wave 21 pipeline against REAL Claude Code session logs (738 local sessions, 66 with is_error:true) rather than only the synthetic fixture. Two outcomes: a collator fix and a permanent validation example. ## Collator fix: empty-recovery error turns On real traces, ~67% of error sites have EMPTY recovery content when strip_thinking=True — because the error-RECOVERY turn is frequently pure [THINKING] (the model reasons about the failure, then silently retries a tool), and stripping thinking empties it. The old code set any_errors=True and injected a hint the moment hint_text existed, BEFORE checking recovery content, so an empty-recovery turn produced an all-ignore_index sdpo_loss_mask: a zero-signal SDPO row that wastes a forward pass and dilutes the channel. Fix: only treat a turn as an SDPO error site when BOTH a hint was produced AND the recovery turn has content (`hint_text and turn.get("content")`). Applied symmetrically to `_build_hint_injected_trace` (te…d61036a