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… scale loss by dp_size for FSDP/Megatron parity Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
…omparison Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Signed-off-by: Justin Yu <justinvyu@anyscale.com>
justinvyu
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Mar 9, 2026
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| # Step 1: Z-score normalization (if enabled) | ||
| if self.cfg.trainer.algorithm.advantage_batch_normalize: | ||
| num_actions = response_mask.sum() | ||
| mean = advantages.mean() | ||
| std = ((advantages - mean).pow(2) * response_mask).sum() | ||
| rstd = (std / num_actions).clamp(min=1e-8).rsqrt() | ||
| advantages = (advantages - mean) * rstd |
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note: this is a bit different from before -- we're taking a per-minibatch z-score, compared to before it's using the epoch-level mean/std
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| # Option 1b: token-mean within each microbatch, then mean across microbatches | ||
| elif self.cfg.trainer.algorithm.loss_reduction == "token_mean_baseline": | ||
| micro_batch_size = self.cfg.trainer.micro_train_batch_size_per_gpu | ||
| num_micro_batches = len(data) // micro_batch_size | ||
| for i in range(num_micro_batches): | ||
| start_idx = i * micro_batch_size | ||
| end_idx = (i + 1) * micro_batch_size | ||
| microbatch_advantages = advantages[start_idx:end_idx] | ||
| microbatch_loss_mask = loss_mask[start_idx:end_idx] | ||
| # Compute token-mean within each microbatch | ||
| microbatch_advantages = microbatch_advantages / microbatch_loss_mask.sum().clamp(min=1) | ||
| # Average across microbatches | ||
| microbatch_advantages /= num_micro_batches | ||
| data["advantages"][start_idx:end_idx] = microbatch_advantages |
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baseline mean across minibatch token mean implementation
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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port over changes from #925 to new directory structure