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, 63 insertions, 0 deletions

diff --git a/collaborativeagents/training/grpo_verl/run_verl_grpo.sh b/collaborativeagents/training/grpo_verl/run_verl_grpo.sh
new file mode 100644
index 0000000..ede35ab
--- /dev/null
+++ b/collaborativeagents/training/grpo_verl/run_verl_grpo.sh
@@ -0,0 +1,63 @@
+#!/bin/bash
+export PYTHONPATH="/shared/storage-01/users/mehri2/verl:$PYTHONPATH"
+set -x
+HYDRA_FULL_ERROR=1
+
+train_data="/shared/storage-01/users/mehri2/mem/collaborativeagents/training/grpo_verl/data/session_level_reflection_grpo_train.parquet"
+model_path="/shared/storage-01/users/mehri2/LLaMA-Factory/saves/llama-3.1-8b-instruct/full/sft_session_level_reflection/checkpoint-628"
+reward_fn_path="/shared/storage-01/users/mehri2/mem/collaborativeagents/training/grpo_verl/verl_reward_functions.py"
+
+max_prompt_length=2048
+max_response_length=1024
+train_batch_size=8
+n_generations=8
+# Effective batch size is 64
+
+python3 -m verl.trainer.main_ppo \
+    algorithm.adv_estimator=grpo \
+    data.train_files="$train_data" \
+    data.val_files="$train_data" \
+    data.train_batch_size=$train_batch_size \
+    data.max_prompt_length=$max_prompt_length \
+    data.max_response_length=$max_response_length \
+    data.filter_overlong_prompts=True \
+    data.truncation='error' \
+    data.prompt_key=prompt \
+    data.reward_fn_key=data_source \
+    actor_rollout_ref.model.path=$model_path \
+    actor_rollout_ref.actor.optim.lr=1e-6 \
+    actor_rollout_ref.model.use_remove_padding=True \
+    actor_rollout_ref.actor.ppo_mini_batch_size=8 \
+    actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=8 \
+    actor_rollout_ref.actor.use_kl_loss=True \
+    actor_rollout_ref.actor.kl_loss_coef=0.003 \
+    actor_rollout_ref.actor.kl_loss_type=low_var_kl \
+    actor_rollout_ref.actor.entropy_coeff=0 \
+    actor_rollout_ref.model.enable_gradient_checkpointing=True \
+    actor_rollout_ref.actor.fsdp_config.model_dtype=bfloat16 \
+    actor_rollout_ref.actor.fsdp_config.param_offload=False \
+    actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \
+    actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=8 \
+    actor_rollout_ref.rollout.tensor_model_parallel_size=1 \
+    actor_rollout_ref.rollout.name=vllm \
+    actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \
+    actor_rollout_ref.rollout.n=$n_generations \
+    actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=8 \
+    actor_rollout_ref.ref.fsdp_config.model_dtype=bfloat16 \
+    actor_rollout_ref.ref.fsdp_config.param_offload=True \
+    actor_rollout_ref.rollout.temperature=0.9 \
+    actor_rollout_ref.rollout.top_p=0.9 \
+    custom_reward_function.path=$reward_fn_path \
+    custom_reward_function.name=compute_score \
+    algorithm.use_kl_in_reward=False \
+    trainer.critic_warmup=0 \
+    trainer.val_before_train=False \
+    trainer.logger='["console","wandb"]' \
+    trainer.project_name='collaborative-agent-reflection-grpo' \
+    trainer.experiment_name='llama3.1-8b-verl-grpo-v3' \
+    trainer.n_gpus_per_node=4 \
+    trainer.nnodes=1 \
+    trainer.save_freq=50 \
+    trainer.test_freq=100 \
+    trainer.total_epochs=1 \
+    trainer.default_local_dir=/shared/storage-01/users/mehri2/mem/collaborativeagents/training/grpo_verl/results/v3 $@
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