feat(rl): add REINFORCE advantage estimator

.github/workflows/pr-test.yml

@@ -372,7 +372,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        info: [{"num_gpus": 0, "test_file": "test_megatron_argument_validation.py"}, {"num_gpus": 0, "test_file": "utils/test_megatron_server_arguments.py"}, {"num_gpus": 0, "test_file": "test_dp_schedule.py"}, {"num_gpus": 0, "test_file": "test_cp_utils.py"}, {"num_gpus": 0, "test_file": "test_metric_report.py"}, {"num_gpus": 0, "test_file": "test_metric_report_dist.py"}, {"num_gpus": 0, "test_file": "test_loss_cp_invariance.py"}, {"num_gpus": 0, "test_file": "test_logprob_response_spans.py"}, {"num_gpus": 0, "test_file": "test_value_temperature.py"}, {"num_gpus": 0, "test_file": "test_cispo_loss.py"}, {"num_gpus": 0, "test_file": "test_rm_f1.py"}, {"num_gpus": 0, "test_file": "test_rm_gpqa.py"}, {"num_gpus": 0, "test_file": "test_rm_math.py"}, {"num_gpus": 0, "test_file": "test_rm_math_dapo.py"}, {"num_gpus": 0, "test_file": "test_rm_deepscaler.py"}, {"num_gpus": 0, "test_file": "test_sample.py"}, {"num_gpus": 0, "test_file": "test_rollout_validation.py"}, {"num_gpus": 0, "test_file": "test_placement_group.py"}, {"num_gpus": 0, "test_file": "test_external_sglang_engines.py"}, {"num_gpus": 0, "test_file": "utils/test_hf_checkpoint_saver.py"}, {"num_gpus": 0, "test_file": "plugin_contracts/test_plugin_rollout_contracts.py"}, {"num_gpus": 0, "test_file": "plugin_contracts/test_plugin_runtime_hook_contracts.py"}, {"num_gpus": 0, "test_file": "plugin_contracts/test_plugin_path_loading_contracts.py"}, {"num_gpus": 0, "test_file": "plugin_contracts/test_plugin_generate_contracts.py"}]
+        info: [{"num_gpus": 0, "test_file": "test_megatron_argument_validation.py"}, {"num_gpus": 0, "test_file": "utils/test_megatron_server_arguments.py"}, {"num_gpus": 0, "test_file": "test_dp_schedule.py"}, {"num_gpus": 0, "test_file": "test_cp_utils.py"}, {"num_gpus": 0, "test_file": "test_metric_report.py"}, {"num_gpus": 0, "test_file": "test_metric_report_dist.py"}, {"num_gpus": 0, "test_file": "test_loss_cp_invariance.py"}, {"num_gpus": 0, "test_file": "test_logprob_response_spans.py"}, {"num_gpus": 0, "test_file": "test_value_temperature.py"}, {"num_gpus": 0, "test_file": "test_cispo_loss.py"}, {"num_gpus": 0, "test_file": "test_reinforce.py"}, {"num_gpus": 0, "test_file": "test_rm_f1.py"}, {"num_gpus": 0, "test_file": "test_rm_gpqa.py"}, {"num_gpus": 0, "test_file": "test_rm_math.py"}, {"num_gpus": 0, "test_file": "test_rm_math_dapo.py"}, {"num_gpus": 0, "test_file": "test_rm_deepscaler.py"}, {"num_gpus": 0, "test_file": "test_sample.py"}, {"num_gpus": 0, "test_file": "test_rollout_validation.py"}, {"num_gpus": 0, "test_file": "test_placement_group.py"}, {"num_gpus": 0, "test_file": "test_external_sglang_engines.py"}, {"num_gpus": 0, "test_file": "utils/test_hf_checkpoint_saver.py"}, {"num_gpus": 0, "test_file": "plugin_contracts/test_plugin_rollout_contracts.py"}, {"num_gpus": 0, "test_file": "plugin_contracts/test_plugin_runtime_hook_contracts.py"}, {"num_gpus": 0, "test_file": "plugin_contracts/test_plugin_path_loading_contracts.py"}, {"num_gpus": 0, "test_file": "plugin_contracts/test_plugin_generate_contracts.py"}]
     defaults:
       run:
         working-directory: ${{ github.workspace }}

.github/workflows/pr-test.yml.j2

@@ -72,6 +72,7 @@
         {'test_file': 'test_logprob_response_spans.py', 'num_gpus': 0},
         {'test_file': 'test_value_temperature.py', 'num_gpus': 0},
         {'test_file': 'test_cispo_loss.py', 'num_gpus': 0},
+        {'test_file': 'test_reinforce.py', 'num_gpus': 0},
         {'test_file': 'test_rm_f1.py', 'num_gpus': 0},
         {'test_file': 'test_rm_gpqa.py', 'num_gpus': 0},
         {'test_file': 'test_rm_math.py', 'num_gpus': 0},

slime/backends/megatron_utils/loss.py

@@ -17,6 +17,7 @@
     compute_gspo_kl,
     compute_opsm_mask,
     compute_policy_loss,
+    compute_reinforce_loss,
     get_advantages_and_returns_batch,
     get_grpo_returns,
     get_reinforce_plus_plus_baseline_advantages,
@@ -713,7 +714,7 @@ def compute_advantages_and_returns(args: Namespace, rollout_data: RolloutBatch)
         custom_adv_fn(args, rollout_data)
         advantages, returns = rollout_data["advantages"], rollout_data["returns"]
 
-    elif args.advantage_estimator in ["grpo", "gspo", "cispo"]:
+    elif args.advantage_estimator in ["grpo", "gspo", "cispo", "reinforce"]:
         rewards = torch.tensor(rewards, dtype=torch.float32, device=kl[0].device)
         returns = get_grpo_returns(rewards, kl)
         # TODO: is the copy necessary?
@@ -973,6 +974,8 @@ def policy_loss_function(
 
     if args.advantage_estimator == "cispo":
         pg_loss, pg_clipfrac = compute_cispo_loss(ppo_kl, log_probs, advantages, args.eps_clip, args.eps_clip_high)
+    elif args.advantage_estimator == "reinforce":
+        pg_loss, pg_clipfrac = compute_reinforce_loss(advantages, log_probs)
     else:
         pg_loss, pg_clipfrac = compute_policy_loss(ppo_kl, advantages, args.eps_clip, args.eps_clip_high)
 

slime/ray/rollout.py

@@ -689,7 +689,7 @@ def _post_process_rewards(self, samples: list[Sample] | list[list[Sample]]):
 
         raw_rewards = [sample.get_reward_value(self.args) for sample in samples]
         if (
-            self.args.advantage_estimator in ["grpo", "gspo", "cispo", "reinforce_plus_plus_baseline"]
+            self.args.advantage_estimator in ["grpo", "gspo", "cispo", "reinforce", "reinforce_plus_plus_baseline"]
             and self.args.rewards_normalization
         ):
             # group norm
@@ -702,7 +702,10 @@ def _post_process_rewards(self, samples: list[Sample] | list[list[Sample]]):
             mean = rewards.mean(dim=-1, keepdim=True)
             rewards = rewards - mean
 
-            if self.args.advantage_estimator in ["grpo", "gspo", "cispo"] and self.args.grpo_std_normalization:
+            if (
+                self.args.advantage_estimator in ["grpo", "gspo", "cispo", "reinforce"]
+                and self.args.grpo_std_normalization
+            ):
                 std = rewards.std(dim=-1, keepdim=True)
                 rewards = rewards / (std + 1e-6)
 

slime/utils/arguments.py

@@ -911,14 +911,17 @@ def add_algo_arguments(parser):
                     "grpo",
                     "gspo",
                     "cispo",
+                    "reinforce",
                     "reinforce_plus_plus",
                     "reinforce_plus_plus_baseline",
                     "ppo",
                 ],
                 default="grpo",
                 help=(
-                    "Advantage estimator to use. Note: on-policy distillation (OPD) is now orthogonal "
-                    "to the advantage estimator. Use --opd-kl-coef > 0 to enable OPD on top of any estimator."
+                    "Advantage estimator to use. 'reinforce' uses GRPO-style group-normalized "
+                    "advantages with the plain additive surrogate (no PPO/IS ratio, no clipping). "
+                    "Note: on-policy distillation (OPD) is now orthogonal to the advantage estimator. "
+                    "Use --opd-kl-coef > 0 to enable OPD on top of any estimator."
                 ),
             )
             parser.add_argument(

slime/utils/ppo_utils.py

@@ -171,6 +171,20 @@ def compute_cispo_loss(
     return pg_losses, clipfrac
 
 
+@torch.compile(dynamic=True)
+def compute_reinforce_loss(
+    advantages: torch.Tensor,
+    log_probs: torch.Tensor,
+):
+    """REINFORCE surrogate ``-A * log pi_theta`` (no IS ratio, no clipping); gradient
+    flows only through ``log_probs``. Same ``(per_token_loss, clipfrac)`` contract as
+    :func:`compute_policy_loss`, with ``clipfrac`` identically zero (nothing is clipped).
+    """
+    pg_losses = -advantages * log_probs
+    clipfrac = torch.zeros_like(pg_losses)
+    return pg_losses, clipfrac
+
+
 def compute_log_probs(
     logits: torch.Tensor,
     tokens: torch.Tensor,

tests/test_reinforce.py

@@ -0,0 +1,35 @@
+"""CPU tests for compute_reinforce_loss (plain ``-A * log pi_theta`` surrogate)."""
+
+import pytest
+import torch
+
+from slime.utils.ppo_utils import compute_reinforce_loss
+
+NUM_GPUS = 0
+
+
+@pytest.mark.unit
+def test_reinforce_loss_matches_closed_form():
+    advantages = torch.tensor([2.0, -1.0, 0.5])
+    log_probs = torch.tensor([-0.1, -0.2, -0.3])
+
+    pg_loss, clipfrac = compute_reinforce_loss(advantages, log_probs)
+
+    assert torch.allclose(pg_loss, -advantages * log_probs)
+    assert torch.allclose(clipfrac, torch.zeros(3))
+
+
+@pytest.mark.unit
+def test_reinforce_gradient_flows_only_through_log_probs():
+    advantages = torch.tensor([2.0, -1.0, 0.5])
+    log_probs = torch.tensor([-0.1, -0.2, -0.3], requires_grad=True)
+
+    pg_loss, _ = compute_reinforce_loss(advantages, log_probs)
+    pg_loss.sum().backward()
+
+    # d/d log_probs [ -A * log_probs ] = -A
+    assert torch.allclose(log_probs.grad, -advantages)
+
+
+if __name__ == "__main__":
+    raise SystemExit(pytest.main([__file__]))