GRPO training: policy_loss / policy_entropy / raw_grad_norm are NaN on every batch (rollout & reward are fine)

[lzt013]

Rollout completes normally and both reward and advantage look correct, but as soon as the policy update step begins, policy_loss, policy_entropy, and raw_grad_norm are all NaN on every single batch — multiple consecutive steps, without exception.

[MakeAI-work]

Hey 👋

I ran DebugCause (our AI debugging agent) against
this issue and wanted to share what it found —
in case it's useful for whoever picks this up.

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Root cause (confirmed)

grouped_rewards.std(dim=1) uses PyTorch's default
unbiased=True (Bessel's correction, divides by n-1).

When num_generations == 1, the grouped rewards
tensor has shape (-1, 1), so std(dim=1) divides
by 1-1 = 0, returning NaN for every group.

The subsequent normalization:

advantages /= std_grouped_rewards + 1e-4

cannot rescue this because NaN + 1e-4 = NaN.

The NaN advantages then propagate into _compute_loss
at line 1455:

per_token_loss1 = coef_1 * advantages.unsqueeze(1)

making the entire policy loss NaN.

scale_rewards defaults to True in arguments.py
line 261, so this path is active by default for
every GRPO training run with num_generations == 1.

The same unbiased=True issue also exists in
_dynamic_sampling at line 1166-1167, though
that call site doesn't trigger NaN — it produces
std=0 for identical-reward groups, which the
existing filtering partially handles.

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Proposed fix

Two changes across grpo_trainer.py:

In _prepare_batch_inputs (~line 1258):

# Before
std_grouped_rewards = grouped_rewards.std(dim=1)
advantages /= std_grouped_rewards + 1e-4

# After
std_grouped_rewards = grouped_rewards.std(dim=1, correction=0)
advantages = (rewards - mean_grouped_rewards) / std_grouped_rewards.clamp(min=1e-4)

In _dynamic_sampling (~line 1166):

# Before
std_grouped_rewards = grouped_rewards.std(dim=1)
zero_std_mask = std_grouped_rewards == 0

# After
std_grouped_rewards = grouped_rewards.std(dim=1, correction=0)
zero_std_mask = std_grouped_rewards == 0

correction=0 switches to population std so
single-element groups return 0.0 instead of NaN.
.clamp(min=1e-4) is strictly more robust than
additive epsilon — it cannot be defeated by NaN
propagation.

Note on semantics: switching from sample std
to population std is a subtle change for groups
with num_generations > 1. For RL advantage
normalization this is standard practice and matches
the original GRPO paper — but worth flagging for
reviewers.

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Regression test

Written and parsed. Reproduces the exact bug:

# With correction=1 (default): advantages are NaN
# With correction=0 + clamp: advantages are finite zeros
def test_advantage_no_nan_with_single_generation():
    num_generations = 1
    rewards_raw = torch.tensor([1.0, 2.0, 3.0, 0.5])
    grouped_rewards = rewards_raw.view(-1, num_generations)
    mean_grouped_rewards = grouped_rewards.mean(dim=1)
    std_grouped_rewards = grouped_rewards.std(dim=1, correction=0)
    advantages = (rewards - mean_grouped_rewards) / std_grouped_rewards.clamp(min=1e-4)
    assert not torch.isnan(advantages).any()

Full test file covers both num_generations == 1
and identical-reward groups with num_generations > 1.

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and produces root cause reports with proposed fixes.

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