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root_scripts/run_rlvr.sh

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+#!/bin/bash
+# One-Shot RLVR: Physics (VLM) - Corrected for absolute paths
+# Based on training_physics_mechanics_oe.sh (canonical VLM adaptation)
+# Usage: bash run_rlvr.sh <MODEL_PATH> <EXPERIMENT_NAME>
+
+set -x
+
+# ==================== Accept CLI args ====================
+MODEL_PATH="${1:-/workspace/rl4phyx/models/Qwen2.5-VL-3B-Instruct}"
+EXPERIMENT_NAME="${2:-rlvr_baseline}"
+
+# ==================== Configuration ====================
+WANDB_PROJECT=${WANDB_PROJECT:-"physics_one_shot_rlvr"}
+BASE="/workspace/rl4phyx/RL4Phyx/ZeroSearch/One-Shot-RLVR"
+DATA_FILE="${BASE}/data/train/physics_vlm/mechanics/mechanics_1_rl_numerical.parquet"
+VAL_FILE="/workspace/rl4phyx/RL4Phyx/oneshot/validation_data/metaphyx_oe_1533.parquet"
+IMAGE_DIR="${BASE}/data/train/physics_vlm/mechanics"
+
+# GPU
+N_GPUS=4
+GPUS="0,1,2,3"
+
+# Training Hyperparameters (aligned with original One-Shot RLVR)
+BATCH_SIZE="128"
+LEARNING_RATE="1e-6"
+ROLLOUT_N="8"
+TEMPERATURE="0.7"
+TENSOR_PARALLEL="2"
+
+# Checkpoints
+CHECKPOINT_DIR="/workspace/rl4phyx/checkpoints/${EXPERIMENT_NAME}"
+
+echo "=========================================="
+echo "One-Shot RLVR: Physics (VLM)"
+echo "Model: ${MODEL_PATH}"
+echo "Experiment: ${EXPERIMENT_NAME}"
+echo "Data: ${DATA_FILE}"
+echo "Checkpoint: ${CHECKPOINT_DIR}"
+echo "=========================================="
+
+mkdir -p $CHECKPOINT_DIR
+mkdir -p /workspace/rl4phyx/logs
+
+# IMPORTANT: Run from /workspace/rl4phyx so Python imports pip veRL v0.7
+# NOT from One-Shot-RLVR dir (which has local verl/ v0.2)
+cd /workspace/rl4phyx
+
+# vLLM backend
+export VLLM_ATTENTION_BACKEND=XFORMERS
+export VLLM_USE_TRITON_FLASH_ATTN=0
+export WANDB_API_KEY=${WANDB_API_KEY:-""}
+
+# ==================== Training ====================
+CUDA_VISIBLE_DEVICES=${GPUS} python3 -m verl.trainer.main_ppo \
+ algorithm.adv_estimator=grpo \
+ +data_domain=physics \
+ data.train_files=${DATA_FILE} \
+ data.val_files=${VAL_FILE} \
+ data.train_batch_size=${BATCH_SIZE} \
+ data.val_batch_size=1533 \
+ data.max_prompt_length=2048 \
+ data.max_response_length=3072 \
+ +data.is_multimodal=True \
+ +data.vlm_model=${MODEL_PATH} \
+ +data.image_dir=${IMAGE_DIR} \
+ reward_model.reward_manager='naive' \
+ actor_rollout_ref.model.path=${MODEL_PATH} \
+ actor_rollout_ref.actor.optim.lr=${LEARNING_RATE} \
+ actor_rollout_ref.model.use_remove_padding=False \
+ actor_rollout_ref.actor.ppo_mini_batch_size=${BATCH_SIZE} \
+ actor_rollout_ref.actor.use_dynamic_bsz=True \
+ actor_rollout_ref.actor.ppo_max_token_len_per_gpu=24000 \
+ actor_rollout_ref.actor.use_kl_loss=True \
+ actor_rollout_ref.actor.kl_loss_coef=0.001 \
+ actor_rollout_ref.actor.kl_loss_type=low_var_kl \
+ actor_rollout_ref.model.enable_gradient_checkpointing=True \
+ actor_rollout_ref.actor.fsdp_config.param_offload=False \
+ +actor_rollout_ref.actor.fsdp_config.grad_offload=False \
+ actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \
+ actor_rollout_ref.rollout.tensor_model_parallel_size=${TENSOR_PARALLEL} \
+ actor_rollout_ref.rollout.name=vllm \
+ actor_rollout_ref.rollout.temperature=${TEMPERATURE} \
+ +actor_rollout_ref.rollout.val_temperature=${TEMPERATURE} \
+ actor_rollout_ref.rollout.gpu_memory_utilization=0.7 \
+ actor_rollout_ref.rollout.n=${ROLLOUT_N} \
+ +actor_rollout_ref.rollout.n_val=1 \
+ actor_rollout_ref.ref.fsdp_config.param_offload=True \
+ algorithm.kl_ctrl.kl_coef=0.001 \
+ trainer.critic_warmup=0 \
+ trainer.logger=['console','wandb'] \
+ trainer.project_name=${WANDB_PROJECT} \
+ trainer.experiment_name=${EXPERIMENT_NAME} \
+ trainer.checkpoints_dir=$CHECKPOINT_DIR \
+ +trainer.val_before_train=True \
+ trainer.n_gpus_per_node=${N_GPUS} \
+ trainer.nnodes=1 \
+ trainer.save_freq=20 \
+ trainer.test_freq=20 \
+ trainer.default_hdfs_dir=null \
+ trainer.total_epochs=2000 2>&1 | tee /workspace/rl4phyx/logs/${EXPERIMENT_NAME}.log