Add collaborativeagents module and update gitignore

- Add collaborativeagents subproject with adapters, agents, and evaluation modules
- Update .gitignore to exclude large binary files (.whl, .tar), wandb logs, and results

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

1 files changed, 124 insertions, 0 deletions

diff --git a/collaborativeagents/training/train_sft.py b/collaborativeagents/training/train_sft.py
new file mode 100644
index 0000000..cff146a
--- /dev/null
+++ b/collaborativeagents/training/train_sft.py
@@ -0,0 +1,124 @@
+#!/usr/bin/env python3
+"""
+SFT Training for Session-Level Reflection using TRL.
+
+This trains the model to generate reflections given conversations,
+which serves as initialization for GRPO training.
+"""
+
+import os
+import json
+import argparse
+from pathlib import Path
+
+os.environ["WANDB_PROJECT"] = "collaborative-agent-reflection-sft"
+
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from trl import SFTTrainer, SFTConfig
+from datasets import Dataset
+
+
+def load_sft_data(data_path: str):
+    """Load SFT training data."""
+    with open(data_path) as f:
+        data = json.load(f)
+
+    # Convert to format expected by SFTTrainer
+    processed = []
+    for item in data:
+        messages = item["messages"]
+        processed.append({"messages": messages})
+
+    return Dataset.from_list(processed)
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Train SFT for session-level reflection")
+    parser.add_argument("--model-path", type=str,
+                       default="/projects/bfqt/users/yurenh2/ml-projects/personalization-user-model/models/llama-3.1-8b-instruct",
+                       help="Path to base model")
+    parser.add_argument("--data-path", type=str, required=True,
+                       help="Path to SFT training data JSON")
+    parser.add_argument("--output-dir", type=str,
+                       default="collaborativeagents/training/outputs/sft_reflection",
+                       help="Output directory for checkpoints")
+    parser.add_argument("--num-epochs", type=int, default=4,
+                       help="Number of training epochs")
+    parser.add_argument("--learning-rate", type=float, default=1e-6,
+                       help="Learning rate")
+    parser.add_argument("--batch-size", type=int, default=1,
+                       help="Per-device batch size")
+    parser.add_argument("--gradient-accumulation", type=int, default=64,
+                       help="Gradient accumulation steps")
+    args = parser.parse_args()
+
+    print(f"Loading model from {args.model_path}...")
+    tokenizer = AutoTokenizer.from_pretrained(args.model_path)
+
+    if tokenizer.pad_token is None:
+        tokenizer.pad_token = tokenizer.eos_token
+
+    # For distributed training (torchrun), don't use device_map
+    # The trainer handles device placement
+    model = AutoModelForCausalLM.from_pretrained(
+        args.model_path,
+        torch_dtype=torch.bfloat16,
+        attn_implementation="sdpa",  # Use PyTorch native scaled dot product attention
+    )
+
+    print(f"Loading training data from {args.data_path}...")
+    dataset = load_sft_data(args.data_path)
+    print(f"Loaded {len(dataset)} examples")
+
+    # Training arguments (from paper Table 4)
+    # Using SFTConfig with assistant_only_loss=True (replaces DataCollatorForCompletionOnlyLM)
+    training_args = SFTConfig(
+        output_dir=args.output_dir,
+        num_train_epochs=args.num_epochs,
+        per_device_train_batch_size=args.batch_size,
+        gradient_accumulation_steps=args.gradient_accumulation,
+        learning_rate=args.learning_rate,
+        lr_scheduler_type="cosine",
+        warmup_ratio=0.1,
+        bf16=True,
+        logging_steps=10,
+        save_steps=100,
+        save_total_limit=3,
+        report_to="wandb",
+        max_grad_norm=1.0,
+        weight_decay=0.01,
+        max_seq_length=4096,
+        # Train only on assistant responses (completion-only loss)
+        # Note: assistant_only_loss needs the tokenizer to identify assistant turns
+        remove_unused_columns=False,
+    )
+
+    # Format function for chat template
+    def formatting_func(example):
+        return tokenizer.apply_chat_template(
+            example["messages"],
+            tokenize=False,
+            add_generation_prompt=False
+        )
+
+    trainer = SFTTrainer(
+        model=model,
+        args=training_args,
+        train_dataset=dataset,
+        processing_class=tokenizer,
+        formatting_func=formatting_func,
+    )
+
+    print("Starting SFT training...")
+    trainer.train()
+
+    # Save final model
+    final_path = Path(args.output_dir) / "final"
+    trainer.save_model(str(final_path))
+    tokenizer.save_pretrained(str(final_path))
+    print(f"Saved final model to {final_path}")
+
+
+if __name__ == "__main__":
+    main()