Remove beta KL divergence from training loss#607
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Remove the Schulman KL estimator (beta * KL) that was added directly to the training loss. The kl_penalty_coef mechanism (advantage adjustment) remains as the preferred approach for KL regularization. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
corbt
approved these changes
Mar 9, 2026
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Summary
betaparameter and Schulman KL divergence estimator (exp(r-n) - (r-n) - 1) that was added directly to the training losskl_penalty_coefmechanism (zero-mean advantage adjustment) remains as the preferred approach for KL regularizationChanges
src/art/types.py: Removebetafield fromTrainConfigsrc/art/loss.py: Removemean_klfromLossclass and the KL divergence computationsrc/art/local/backend.py: Removebetaparameter fromLocalBackend.train()src/art/serverless/backend.py: Removebetaparameter fromServerlessBackend.train()src/art/unsloth/train.py: Removebeta * mean_klloss addition andkl_divmetric loggingsrc/art/megatron/train.py: Removebeta * mean_klloss additionsrc/art/preprocessing/inputs.py: Removebetafrom warmup config overrideTest plan
uv run prek run --all-filespasses locally (ruff, ruff format, ty)test_backend_train_api.pypassed on H200 GPU cluster — model registration, trajectory gathering, training, and logging all succeeded🤖 Generated with Claude Code