diff --git a/dspy/clients/lm_local.py b/dspy/clients/lm_local.py
index c937ee6e5a..698c4b44e0 100644
--- a/dspy/clients/lm_local.py
+++ b/dspy/clients/lm_local.py
@@ -253,25 +253,43 @@ def tokenize_function(example):
             task_type="CAUSAL_LM",
         )
 
-    sft_config = SFTConfig(
-        output_dir=train_kwargs["output_dir"],
-        num_train_epochs=train_kwargs["num_train_epochs"],
-        per_device_train_batch_size=train_kwargs["per_device_train_batch_size"],
-        gradient_accumulation_steps=train_kwargs["gradient_accumulation_steps"],
-        learning_rate=train_kwargs["learning_rate"],
-        max_grad_norm=2.0,  # note that the current SFTConfig default is 1.0
-        logging_steps=20,
-        warmup_ratio=0.03,
-        lr_scheduler_type="constant",
-        save_steps=10_000,
-        bf16=train_kwargs["bf16"],
-        max_seq_length=train_kwargs["max_seq_length"],
-        packing=train_kwargs["packing"],
-        dataset_kwargs={  # We need to pass dataset_kwargs because we are processing the dataset ourselves
+    # Handle compatibility between different TRL versions
+    # TRL >= 0.16.0 uses 'max_length' instead of 'max_seq_length' in SFTConfig
+    import inspect
+    sft_config_params = inspect.signature(SFTConfig.__init__).parameters
+
+    # Build config parameters, handling the max_seq_length -> max_length change
+    config_kwargs = {
+        "output_dir": train_kwargs["output_dir"],
+        "num_train_epochs": train_kwargs["num_train_epochs"],
+        "per_device_train_batch_size": train_kwargs["per_device_train_batch_size"],
+        "gradient_accumulation_steps": train_kwargs["gradient_accumulation_steps"],
+        "learning_rate": train_kwargs["learning_rate"],
+        "max_grad_norm": 2.0,  # note that the current SFTConfig default is 1.0
+        "logging_steps": 20,
+        "warmup_ratio": 0.03,
+        "lr_scheduler_type": "constant",
+        "save_steps": 10_000,
+        "bf16": train_kwargs["bf16"],
+        "packing": train_kwargs["packing"],
+        "dataset_kwargs": {  # We need to pass dataset_kwargs because we are processing the dataset ourselves
             "add_special_tokens": False,  # Special tokens handled by template
             "append_concat_token": False,  # No additional separator needed
         },
-    )
+    }
+
+    # Add the sequence length parameter using the appropriate name for the TRL version
+    if "max_seq_length" in sft_config_params:
+        # Older TRL versions (< 0.16.0)
+        config_kwargs["max_seq_length"] = train_kwargs["max_seq_length"]
+    elif "max_length" in sft_config_params:
+        # Newer TRL versions (>= 0.16.0)
+        config_kwargs["max_length"] = train_kwargs["max_seq_length"]
+    else:
+        logger.warning("Neither 'max_seq_length' nor 'max_length' parameter found in SFTConfig. "
+                      "This may indicate an incompatible TRL version.")
+
+    sft_config = SFTConfig(**config_kwargs)
 
     logger.info("Starting training")
     trainer = SFTTrainer(