feat(trl): improve eval dataset handling and documentation

- Add train/eval split to all training examples (SFT, DPO)
- Add eval_strategy configuration with proper dataset requirements
- Add critical troubleshooting section for training hangs
- Improve documentation with examples of correct vs incorrect patterns
- Make utility scripts executable (convert_to_gguf, estimate_cost, validate_dataset)
- Update references to point to example scripts for production training
- Remove outdated 'Quick Demo' and 'Production with Checkpoints' sections

FIXES: Training jobs hanging when eval_strategy is set without eval_dataset
IMPROVES: User guidance on best practices for monitoring and evaluation
ADDS: Comprehensive troubleshooting guide for common issues

trl/references/training_methods.md

@@ -24,11 +24,14 @@ trainer = SFTTrainer(
         output_dir="my-model",
         push_to_hub=True,
         hub_model_id="username/my-model",
+        eval_strategy="no",  # Disable eval for simple example
     )
 )
 trainer.train()
 ```
 
+**Note:** For production training with evaluation monitoring, see `scripts/train_sft_example.py`
+
 **Documentation:** `hf_doc_fetch("https://huggingface.co/docs/trl/sft_trainer")`
 
 ## Direct Preference Optimization (DPO)
@@ -52,11 +55,14 @@ trainer = DPOTrainer(
     args=DPOConfig(
         output_dir="dpo-model",
         beta=0.1,  # KL penalty coefficient
+        eval_strategy="no",  # Disable eval for simple example
     )
 )
 trainer.train()
 ```
 
+**Note:** For production training with evaluation monitoring, see `scripts/train_dpo_example.py`
+
 **Documentation:** `hf_doc_fetch("https://huggingface.co/docs/trl/dpo_trainer")`
 
 ## Group Relative Policy Optimization (GRPO)