feat: force on-policy ratio to 1

examples/configs/grpo_math_1B.yaml

@@ -53,6 +53,7 @@ loss_fn:
   truncated_importance_sampling_ratio: null
   sequence_level_importance_ratios: false
   token_level_loss: true
+  force_on_policy_ratio: false  # Set to true to force ratio=1.0 (requires train_global_batch_size == num_prompts_per_step * num_generations_per_prompt)
 
 checkpointing:
   enabled: true

examples/configs/vlm_grpo_3B.yaml

@@ -46,6 +46,7 @@ loss_fn:
   use_importance_sampling_correction: false
   truncated_importance_sampling_ratio: null
   token_level_loss: true
+  force_on_policy_ratio: false  # Set to true to force ratio=1.0 (requires train_global_batch_size == num_prompts_per_step * num_generations_per_prompt)
 
 checkpointing:
   enabled: true

examples/configs/vlm_grpo_3B_megatron.yaml

@@ -42,6 +42,7 @@ loss_fn:
   use_importance_sampling_correction: false
   truncated_importance_sampling_ratio: null
   token_level_loss: true
+  force_on_policy_ratio: false  # Set to true to force ratio=1.0 (requires train_global_batch_size == num_prompts_per_step * num_generations_per_prompt)
 checkpointing:
   enabled: true
   checkpoint_dir: results/clevr_grpo_${policy.model_name}

examples/penguin/grpo_dapo17k_bytedtsinghua_qwen3_4binstruct_nf.yaml

@@ -41,6 +41,7 @@ loss_fn:
   truncated_importance_sampling_ratio: null
   use_importance_sampling_correction: false
   token_level_loss: true
+  force_on_policy_ratio: false  # Set to true to force ratio=1.0 (requires train_global_batch_size == num_prompts_per_step * num_generations_per_prompt)
 
 checkpointing:
   enabled: true

nemo_rl/algorithms/grpo.py

@@ -582,6 +582,17 @@ def init_vllm():
 
     loss_fn = ClippedPGLossFn(loss_config)
 
+    # Validate force_on_policy_ratio
+    if loss_config.get("force_on_policy_ratio", False):
+        assert (
+            grpo_config["num_prompts_per_step"]
+            * grpo_config["num_generations_per_prompt"]
+            == policy_config["train_global_batch_size"]
+        ), (
+            "force_on_policy_ratio requires train_global_batch_size == num_prompts_per_step * num_generations_per_prompt"
+        )
+        print("  ✓ force_on_policy_ratio enabled")
+
     # Calculate total setup time
     total_setup_time = time.perf_counter() - setup_start_time
     worker_init_timing_metrics["total_setup_time_s"] = total_setup_time
@@ -1342,7 +1353,17 @@ def grpo_train(
 
                 metrics.update(train_results["all_mb_metrics"])
                 for k, v in metrics.items():
-                    if k in {
+                    if k in {"probs_ratio_min", "probs_ratio_clamped_min"}:
+                        valid_values = [x for x in v if not np.isinf(x)]
+                        metrics[k] = (
+                            np.min(valid_values).item() if valid_values else -1.0
+                        )
+                    elif k in {"probs_ratio_max", "probs_ratio_clamped_max"}:
+                        valid_values = [x for x in v if not np.isinf(x)]
+                        metrics[k] = (
+                            np.max(valid_values).item() if valid_values else -1.0
+                        )
+                    elif k in {
                         "lr",
                         "wd",
                         "reward",
@@ -2270,7 +2291,17 @@ def async_grpo_train(
                 }
                 metrics.update(train_results["all_mb_metrics"])
                 for k, v in metrics.items():
-                    if k in {
+                    if k in {"probs_ratio_min", "probs_ratio_clamped_min"}:
+                        valid_values = [x for x in v if not np.isinf(x)]
+                        metrics[k] = (
+                            np.min(valid_values).item() if valid_values else -1.0
+                        )
+                    elif k in {"probs_ratio_max", "probs_ratio_clamped_max"}:
+                        valid_values = [x for x in v if not np.isinf(x)]
+                        metrics[k] = (
+                            np.max(valid_values).item() if valid_values else -1.0
+                        )
+                    elif k in {
                         "lr",
                         "wd",
                         "reward",

nemo_rl/algorithms/loss_functions.py

@@ -11,6 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import math
 from typing import Any, NotRequired, Optional, TypedDict, TypeVar
 
 import torch
@@ -45,11 +46,18 @@ class ClippedPGLossConfig(TypedDict):
     use_importance_sampling_correction: bool
     truncated_importance_sampling_ratio: float | None
     token_level_loss: bool
+    force_on_policy_ratio: bool
     # If True, apply the off-policy importance-sampling correction at the
     # sequence level (one weight per generated sample), as in GSPO.
     # If False (default), correction is applied at the token level as in the
     # original GRPO paper.
     sequence_level_importance_ratios: NotRequired[bool]
+    disable_ppo_ratio: NotRequired[bool]
+    # If True, force the ratio to 1.0 for truly on-policy behavior,
+    # eliminating any importance sampling effects.
+    # NOTE: This should only be used when doing exactly one update per rollout
+    # (i.e., num_prompts_per_step * num_generations_per_prompt == train_global_batch_size)
+    force_on_policy_ratio: NotRequired[bool]
 
 
 class ClippedPGLossDataDict(TypedDict):
@@ -74,6 +82,7 @@ class ClippedPGLossFn(LossFunction):
     - GRPO - https://arxiv.org/abs/2402.03300
     - REINFORCE/RLOO (set disable_ppo_ratio = True and ignores ratio_clip_min/ratio_clip_max) - https://arxiv.org/abs/2402.14740
     - GSPO (set sequence_level_importance_ratios = True and token_level_loss = False) - https://arxiv.org/abs/2507.18071
+    - Truly on-policy (set force_on_policy_ratio = True to force ratio = 1.0, requires one update per rollout)
 
     Formula:
     L(θ) = E_t [ min(r_t(θ) * A_t, clip(r_t(θ), 1-ε, 1+ε) * A_t) ] - β * KL(π_θ || π_ref)
@@ -114,6 +123,9 @@ def __init__(self, cfg: ClippedPGLossConfig):
         self.kl_input_clamp_value = cfg["kl_input_clamp_value"]
         self.kl_output_clamp_value = cfg["kl_output_clamp_value"]
         self.disable_ppo_ratio = cfg.get("disable_ppo_ratio", False)
+        self.force_on_policy_ratio = cfg.get(
+            "force_on_policy_ratio", False
+        )  # Force ratio to 1.0
         self.use_on_policy_kl_approximation = cfg["use_on_policy_kl_approximation"]
         self.use_importance_sampling_correction = cfg[
             "use_importance_sampling_correction"
@@ -296,7 +308,13 @@ def __call__(
             kl = torch.tensor(0.0)
 
         # Calculate clipped loss function if ppo ratio is enabled.
-        if not self.disable_ppo_ratio:
+        if self.force_on_policy_ratio:
+            # Force ratio to 1.0 for truly on-policy behavior
+            # Use curr_logprobs twice so ratio=1 but gradients still flow
+            log_ratios = curr_logprobs - curr_logprobs.detach()
+            ratios = log_ratios.exp()  # = exp(0) = 1.0, but depends on curr_logprobs
+            ratios_clamped = ratios
+        elif not self.disable_ppo_ratio:
             log_ratios = curr_logprobs - prev_logprobs
             if self.sequence_level_importance_ratios:
                 seq_log_ratio_mean = masked_mean(
@@ -419,6 +437,22 @@ def __call__(
                 global_normalization_factor=global_valid_toks,
             ).item()
 
+            # Calculate min/max values for ratios (only for valid tokens)
+            masked_ratios = ratios.detach()[mask.bool()]
+            masked_ratios_clamped = ratios_clamped.detach()[mask.bool()]
+
+            # Handle edge case where there might be no valid tokens
+            if masked_ratios.numel() > 0:
+                probs_ratio_min = masked_ratios.min().item()
+                probs_ratio_max = masked_ratios.max().item()
+                probs_ratio_clamped_min = masked_ratios_clamped.min().item()
+                probs_ratio_clamped_max = masked_ratios_clamped.max().item()
+            else:
+                probs_ratio_min = float("inf")
+                probs_ratio_max = float("-inf")
+                probs_ratio_clamped_min = float("inf")
+                probs_ratio_clamped_max = float("-inf")
+
         # If you provided a global_valid_{seqs/toks}, all metrics here are globally normalized
         # by either sequence or token count, depending on particular metric.
         # To get the true metric, you'll need to sum over the microbatch.
@@ -428,6 +462,10 @@ def __call__(
                 "loss": loss.item(),
                 "probs_ratio": probs_ratio,
                 "probs_ratio_clamped": probs_ratio_clamped,
+                "probs_ratio_min": probs_ratio_min,
+                "probs_ratio_max": probs_ratio_max,
+                "probs_ratio_clamped_min": probs_ratio_clamped_min,
+                "probs_ratio_clamped_max": probs_ratio_clamped_max,
                 "kl_penalty": kl.item() / self.reference_policy_kl_penalty if kl else 0,
                 "token_mult_prob_error": mult_prob_error,
                 "gen_kl_error": gen_kl_error,
@@ -903,8 +941,24 @@ def __call__(
             loss_accum += loss
             for k, v in metrics.items():
                 if k not in metrics_accum:
-                    metrics_accum[k] = 0
-                metrics_accum[k] += v
+                    if k in {"probs_ratio_min", "probs_ratio_clamped_min"}:
+                        metrics_accum[k] = float("inf")
+                    elif k in {"probs_ratio_max", "probs_ratio_clamped_max"}:
+                        metrics_accum[k] = float("-inf")
+                    else:
+                        metrics_accum[k] = 0
+
+                val = v.item() if isinstance(v, torch.Tensor) and v.ndim == 0 else v
+
+                # Skip inf/-inf sentinel values (from sequences with no valid tokens)
+                if k in {"probs_ratio_min", "probs_ratio_clamped_min"}:
+                    if not math.isinf(val):
+                        metrics_accum[k] = min(metrics_accum[k], val)
+                elif k in {"probs_ratio_max", "probs_ratio_clamped_max"}:
+                    if not math.isinf(val):
+                        metrics_accum[k] = max(metrics_accum[k], val)
+                else:
+                    metrics_accum[k] += val
 
         return loss_accum, metrics_accum
 

tests/unit/algorithms/test_grpo.py

@@ -897,6 +897,7 @@ def val_iter(self):
             "truncated_importance_sampling_ratio": None,
             "sequence_level_importance_ratios": False,
             "token_level_loss": True,
+            "force_on_policy_ratio": False,
         }
     )
     logger = MagicMock()

tests/unit/algorithms/test_loss_functions.py

@@ -41,6 +41,7 @@
     "truncated_importance_sampling_ratio": None,  # Disable TIS
     "sequence_level_importance_ratios": False,
     "token_level_loss": True,
+    "force_on_policy_ratio": False,
 }
 
 
@@ -562,6 +563,61 @@ def test_clipped_pg_loss_reinforce_mode():
     torch.testing.assert_close(actual_loss, expected_loss)
 
 
+def test_clipped_pg_loss_force_on_policy_ratio():
+    """Tests that force_on_policy_ratio forces ratios to 1.0 while keeping gradients."""
+    if not torch.cuda.is_available():
+        pytest.skip("No GPU available")
+
+    device = "cuda"
+    data, batch_size, seq_len, vocab_size = _setup_clipped_pg_test_data(device=device)
+
+    cfg = deepcopy(basic_pg_loss_test_config)
+    cfg["force_on_policy_ratio"] = True
+    loss_fn = ClippedPGLossFn(cfg)
+
+    # Use same logprob pattern as PPO clipping test to ensure
+    # that without the flag, ratios would be [0.5, 1.0, 1.5]
+    adv_masked = torch.tensor([[1.0, -1.0, 2.0]], device=device)
+    prev_lp_masked = torch.tensor([[-1.0, -1.0, -1.0]], device=device)
+    curr_lp_masked = torch.tensor(
+        [[-1.69315, -1.0, -0.59453]], device=device
+    )  # approx log(0.5)-1, log(1)-1, log(1.5)-1
+
+    # Fill full tensors (only need first dim for B=1)
+    data["advantages"][0, 1:] = adv_masked
+    data["prev_logprobs"][0, 1:] = prev_lp_masked
+
+    # Hand-calculated expected loss when ratios are forced to 1.0
+    ratios = torch.ones_like(adv_masked, device=device)
+    loss_per_token = -adv_masked * ratios  # [-1.0, 1.0, -2.0]
+    expected_loss = torch.mean(loss_per_token)  # (-1 + 1 - 2) / 3 = -0.6666...
+
+    input_ids = data["input_ids"]
+    dummy_logits = _create_exact_logits(
+        curr_lp_masked, input_ids, batch_size, seq_len, vocab_size, device
+    )
+
+    actual_loss, metrics = loss_fn(
+        dummy_logits,
+        data,
+        global_valid_seqs=torch.sum(data["sample_mask"]),
+        global_valid_toks=torch.sum(
+            data["sample_mask"].unsqueeze(-1) * data["token_mask"]
+        ),
+    )
+
+    # Loss should match the on-policy expectation
+    torch.testing.assert_close(actual_loss, expected_loss, rtol=1e-3, atol=1e-3)
+
+    # Ratios and their metrics should all be exactly 1.0
+    assert metrics["probs_ratio"] == 1.0
+    assert metrics["probs_ratio_clamped"] == 1.0
+    assert metrics["probs_ratio_min"] == 1.0
+    assert metrics["probs_ratio_max"] == 1.0
+    assert metrics["probs_ratio_clamped_min"] == 1.0
+    assert metrics["probs_ratio_clamped_max"] == 1.0
+
+
 @pytest.mark.parametrize("kl_type", ["k1", "k2", "k3"])
 def test_calculate_kl(kl_type):
     """Tests KL calculations."""

tests/unit/algorithms/test_sequence_packing_gradients.py

@@ -139,6 +139,7 @@ def test_sequence_packing_gradients(self):
             "truncated_importance_sampling_ratio": None,
             "sequence_level_importance_ratios": False,
             "token_level_loss": True,
+            "force_on_policy_ratio": False,
         }
 
         base_loss_fn = ClippedPGLossFn(loss_config)

tests/unit/models/policy/test_dtensor_worker.py

@@ -681,6 +681,7 @@ def test_dtensor_loss_independent_of_microbatch_size_two_gpus(
             "truncated_importance_sampling_ratio": None,
             "sequence_level_importance_ratios": False,
             "token_level_loss": True,
+            "force_on_policy_ratio": False,
         }
     )
 

tests/unit/models/policy/test_megatron_worker.py

@@ -50,6 +50,7 @@
     "truncated_importance_sampling_ratio": None,
     "sequence_level_importance_ratios": False,
     "token_level_loss": True,
+    "force_on_policy_ratio": False,
 }