diff --git a/src/prime_rl/orchestrator/orchestrator.py b/src/prime_rl/orchestrator/orchestrator.py
index 7919764b0c..82fd426122 100644
--- a/src/prime_rl/orchestrator/orchestrator.py
+++ b/src/prime_rl/orchestrator/orchestrator.py
@@ -602,6 +602,7 @@ def process_rollout(rollout: vf.RolloutOutput, rollout_idx: int) -> list[Trainin
                 "reward": [rollout["reward"] for rollout in train_rollouts],
                 "is_truncated": [rollout["is_truncated"] for rollout in train_rollouts],
                 "error": [rollout["error"] for rollout in train_rollouts],
+                "stop_condition": [rollout.get("stop_condition") for rollout in train_rollouts],
                 "seq_len": [get_seq_len(rollout) for rollout in train_rollouts],
                 "prefill_len": rollout_prefill_lens,
                 "decode_len": rollout_decode_lens,
@@ -666,6 +667,14 @@ def process_rollout(rollout: vf.RolloutOutput, rollout_idx: int) -> list[Trainin
             "is_truncated/mean": results_df.groupby("example_id").is_truncated.mean().mean(),
             "is_truncated/max": results_df.groupby("example_id").is_truncated.mean().max(),
             "is_truncated/min": results_df.groupby("example_id").is_truncated.mean().min(),
+            "stop_condition/generation_truncated": (
+                results_df.is_truncated & (results_df.stop_condition != "prompt_too_long")
+            ).mean(),
+            # Log rate of each stop condition (e.g. max_turns, prompt_too_long, has_error)
+            **{
+                f"stop_condition/{sc}": rate
+                for sc, rate in results_df.stop_condition.dropna().value_counts(normalize=True).items()
+            },
             # Seqs per rollout metrics
             "samples_per_rollout/mean": results_df.groupby("example_id").samples_per_rollout.mean().mean(),
             "samples_per_rollout/max": results_df.groupby("example_id").samples_per_rollout.mean().max(),