diff --git a/src/post_training/methods/common.py b/src/post_training/methods/common.py
index 4cf0db4..75a95ba 100644
--- a/src/post_training/methods/common.py
+++ b/src/post_training/methods/common.py
@@ -126,6 +126,54 @@ def build_common_training_kwargs(
     )
 
 
+def sanitize_generation_config(trainer: Any) -> None:
+    """Fix inconsistent ``generation_config`` so checkpoint saves don't fail.
+
+    Some upstream models (notably Olmo-3 Think variants) ship a
+    ``generation_config.json`` that sets sampling-only parameters
+    (``temperature``, ``top_p``) while leaving ``do_sample=False``.  This is
+    benign at training time — we never call ``model.generate`` — but
+    ``transformers >= 5.x`` runs strict validation inside
+    ``GenerationConfig.save_pretrained`` and refuses to write the file::
+
+        ValueError: GenerationConfig is invalid:
+          - `temperature` is set to 0.6 -- this flag is only used in
+            sample-based generation modes. You should set `do_sample=True`
+            or unset `temperature`.
+
+    Every checkpoint save ultimately calls ``model.save_pretrained`` which
+    writes the generation config, so an unfixed model crashes the very first
+    save.  We patch ``do_sample`` to ``True`` once, on the in-memory model
+    object, immediately after the trainer has been constructed.  The fix is
+    local to this run — the upstream model files on the Hub are unchanged.
+    """
+    model = getattr(trainer, "model", None)
+    if model is None:
+        return
+    gc = getattr(model, "generation_config", None)
+    if gc is None:
+        return
+    _FLOAT_SAMPLING_PARAMS = (
+        "temperature",
+        "top_p",
+        "min_p",
+        "top_h",
+        "typical_p",
+        "epsilon_cutoff",
+        "eta_cutoff",
+    )
+    has_sampling_param = any(
+        getattr(gc, p, None) is not None for p in _FLOAT_SAMPLING_PARAMS
+    ) or getattr(gc, "top_k", None) not in (None, 0)
+    if has_sampling_param and not getattr(gc, "do_sample", False):
+        logger.info(
+            "Sanitizing generation_config: setting do_sample=True so that "
+            "checkpoint saves can write generation_config.json without "
+            "tripping transformers' strict validation."
+        )
+        gc.do_sample = True
+
+
 def build_callbacks(config: PostTrainingConfig, run_dir: Path) -> list:
     """Build the callback list (shared across methods)."""
     callbacks: list = []
diff --git a/src/post_training/methods/dpo.py b/src/post_training/methods/dpo.py
index bcdb125..509064f 100644
--- a/src/post_training/methods/dpo.py
+++ b/src/post_training/methods/dpo.py
@@ -14,6 +14,7 @@
     build_common_training_kwargs,
     build_model_init_kwargs,
     build_tokenizer,
+    sanitize_generation_config,
 )
 
 if TYPE_CHECKING:
@@ -56,7 +57,7 @@ def build_dpo_trainer(config: PostTrainingConfig, run_dir: Path) -> DPOTrainer:
         model_init_kwargs=build_model_init_kwargs(config),
     )
 
-    return DPOTrainer(
+    trainer = DPOTrainer(
         model=config.model.name_or_path,
         ref_model=mc.ref_model_name_or_path,  # None → TRL creates implicit copy
         processing_class=tokenizer,
@@ -64,3 +65,5 @@ def build_dpo_trainer(config: PostTrainingConfig, run_dir: Path) -> DPOTrainer:
         args=dpo_config,
         callbacks=build_callbacks(config, run_dir),
     )
+    sanitize_generation_config(trainer)
+    return trainer
diff --git a/src/post_training/methods/sft.py b/src/post_training/methods/sft.py
index a34aa68..7f57cbe 100644
--- a/src/post_training/methods/sft.py
+++ b/src/post_training/methods/sft.py
@@ -14,6 +14,7 @@
     build_common_training_kwargs,
     build_model_init_kwargs,
     build_tokenizer,
+    sanitize_generation_config,
 )
 
 if TYPE_CHECKING:
@@ -55,10 +56,13 @@ def build_sft_trainer(config: PostTrainingConfig, run_dir: Path) -> SFTTrainer:
         model_init_kwargs=build_model_init_kwargs(config),
     )
 
-    return SFTTrainer(
+    trainer = SFTTrainer(
         model=config.model.name_or_path,
         processing_class=tokenizer,
         train_dataset=dataset,
         args=sft_config,
         callbacks=build_callbacks(config, run_dir),
     )
+
+    sanitize_generation_config(trainer)
+    return trainer