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之前的代码版本/注意力关键词用于sam的/MedEvalKit/LLaVA-NeXT/llava-critic-r1/EasyR1/Makefile

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+.PHONY: build commit quality style
+
+check_dirs := examples scripts verl setup.py
+
+build:
+	python3 setup.py sdist bdist_wheel
+
+commit:
+	pre-commit install
+	pre-commit run --all-files
+
+quality:
+	ruff check $(check_dirs)
+	ruff format --check $(check_dirs)
+
+style:
+	ruff check $(check_dirs) --fix
+	ruff format $(check_dirs)

之前的代码版本/注意力关键词用于sam的/MedEvalKit/LLaVA-NeXT/llava-critic-r1/EasyR1/README.md

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+# EasyR1 for LLaVA-Critic-R1
+
+This codebase is a customized extension of [EasyR1](https://github.com/hiyouga/EasyR1), specifically modified to support RL training using LLaMA-3.2-Vision-11B-Instruct as the base model. For RL training using Qwen-2.5-VL and Mimo-VL as base models, please use the original EasyR1 codebase. We will integrate support for all models into a unified codebase in the future.
+
+## Training based on Qwen-2.5-VL
+
+```
+bash ./examples/qwen2_5_vl_7b_llava_critic_grpo.sh
+```
+
+## Training based on ThinkLite-VL
+
+```
+bash ./examples/thinklite_vl_7b_llava_critic_grpo.sh
+```
+
+## Training based on Mimo-VL
+
+### SFT checkpoint
+```
+bash ./examples/mimo_vl_7b_llava_critic_grpo.sh
+```
+
+### RL checkpoint
+```
+bash ./examples/mimo_vl_rl_7b_llava_critic_grpo.sh
+```
+
+## Training based on LLaMA-3.2-Vision
+
+```
+bash ./examples/llama3.2v_critic_grpo.sh
+```

之前的代码版本/注意力关键词用于sam的/MedEvalKit/LLaVA-NeXT/llava-critic-r1/EasyR1/examples/baselines/qwen2_5_vl_3b_clevr.sh

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+#!/bin/bash
+
+set -x
+
+export PYTHONUNBUFFERED=1
+
+MODEL_PATH=Qwen/Qwen2.5-VL-3B-Instruct  # replace it with your local file path
+
+python3 -m verl.trainer.main \
+    config=examples/config.yaml \
+    data.train_files=BUAADreamer/clevr_count_70k@train \
+    data.val_files=BUAADreamer/clevr_count_70k@test \
+    data.format_prompt=./examples/format_prompt/r1v_format.jinja \
+    worker.actor.model.model_path=${MODEL_PATH} \
+    worker.rollout.tensor_parallel_size=1 \
+    worker.reward.reward_type=sequential \
+    worker.reward.reward_function=./examples/reward_function/r1v.py:compute_score \
+    trainer.experiment_name=qwen2_5_vl_3b_clevr \
+    trainer.n_gpus_per_node=2

之前的代码版本/注意力关键词用于sam的/MedEvalKit/LLaVA-NeXT/llava-critic-r1/EasyR1/examples/baselines/qwen2_5_vl_3b_geoqa8k.sh

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+#!/bin/bash
+
+set -x
+
+export PYTHONUNBUFFERED=1
+
+MODEL_PATH=Qwen/Qwen2.5-VL-3B-Instruct  # replace it with your local file path
+
+python3 -m verl.trainer.main \
+    config=examples/config.yaml \
+    data.train_files=leonardPKU/GEOQA_8K_R1V@train \
+    data.val_files=leonardPKU/GEOQA_8K_R1V@test \
+    data.format_prompt=./examples/format_prompt/r1v_format.jinja \
+    worker.actor.model.model_path=${MODEL_PATH} \
+    worker.rollout.tensor_parallel_size=1 \
+    worker.reward.reward_type=sequential \
+    worker.reward.reward_function=./examples/reward_function/r1v.py:compute_score \
+    trainer.experiment_name=qwen2_5_vl_3b_geoqa8k \
+    trainer.n_gpus_per_node=8

之前的代码版本/注意力关键词用于sam的/MedEvalKit/LLaVA-NeXT/llava-critic-r1/EasyR1/examples/config.yaml

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+data:
+  train_files: hiyouga/math12k@train
+  val_files: hiyouga/math12k@test
+  prompt_key: problem
+  answer_key: answer
+  image_key: image
+  max_prompt_length: 4096
+  max_response_length: 2048
+  rollout_batch_size: 256
+  val_batch_size: 1024
+  format_prompt: ./examples/format_prompt/math_format.jinja
+  override_chat_template: null
+  shuffle: true
+  seed: 1
+  max_pixels: 4194304
+  min_pixels: 262144
+  filter_overlong_prompts: true
+
+algorithm:
+  adv_estimator: grpo
+  disable_kl: false
+  use_kl_loss: true
+  kl_penalty: low_var_kl
+  kl_coef: 1.0e-2
+
+worker:
+  actor:
+    global_batch_size: 128
+    micro_batch_size_per_device_for_update: 4
+    micro_batch_size_per_device_for_experience: 8
+    max_grad_norm: 1.0
+    padding_free: true
+    ulysses_sequence_parallel_size: 1
+    is_entropy_reg: False
+    entropy_coef: 0.001
+    model:
+      model_path: Qwen/Qwen2.5-7B-Instruct
+      enable_gradient_checkpointing: true
+      trust_remote_code: true
+      freeze_vision_tower: false
+    optim:
+      lr: 1.0e-6
+      weight_decay: 1.0e-2
+      strategy: adamw  # {adamw, adamw_bf16}
+      lr_warmup_ratio: 0.0
+    fsdp:
+      enable_full_shard: true
+      enable_cpu_offload: false
+      enable_rank0_init: true
+    offload:
+      offload_params: true  # true: more CPU memory; false: more GPU memory
+      offload_optimizer: true  # true: more CPU memory; false: more GPU memory
+
+  rollout:
+    n: 8
+    temperature: 1.0
+    top_p: 0.99
+    gpu_memory_utilization: 0.6
+    max_num_batched_tokens: 8192
+    max_model_len: 8192
+    enforce_eager: false
+    enable_chunked_prefill: false
+    tensor_parallel_size: 2
+    limit_images: 10
+    val_override_config:
+      temperature: 0.5
+      n: 1
+
+  ref:
+    fsdp:
+      enable_full_shard: true
+      enable_cpu_offload: true  # true: more CPU memory; false: more GPU memory
+      enable_rank0_init: true
+    offload:
+      offload_params: false
+
+  reward:
+    reward_type: batch
+    reward_function: ./examples/reward_function/math.py:compute_score
+
+trainer:
+  total_epochs: 15
+  max_steps: null
+  project_name: easy_r1
+  experiment_name: qwen2_5_7b_math_grpo
+  logger: ["console", "wandb"]
+  nnodes: 1
+  n_gpus_per_node: 8
+  val_freq: -1  # -1 to disable
+  val_before_train: false
+  val_only: false
+  val_generations_to_log: 3
+  save_freq: 10  # -1 to disable
+  save_limit: -1  # -1 to disable
+  save_checkpoint_path: null
+  load_checkpoint_path: null

之前的代码版本/注意力关键词用于sam的/MedEvalKit/LLaVA-NeXT/llava-critic-r1/EasyR1/examples/format_prompt/caption_format.jinja

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+{{ content | trim }} You FIRST describe the given image in a detailed manner, THEN think about the reasoning process as an internal monologue based on the generated description and given question and then provide the final answer. The description MUST BE enclosed within <caption> </caption> tags. The reasoning process MUST BE enclosed within <think> </think> tags. The final answer MUST BE put in \boxed{}.

之前的代码版本/注意力关键词用于sam的/MedEvalKit/LLaVA-NeXT/llava-critic-r1/EasyR1/examples/llama3.2v_critic_grpo.sh

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+#!/bin/bash
+
+set -x
+
+export PYTHONUNBUFFERED=1
+
+MODEL_PATH=meta-llama/Llama-3.2-11B-Vision-Instruct # replace it with your local file path
+
+python3 -m verl.trainer.main \
+    config=examples/config.yaml \
+    data.train_files=lmms-lab/LLaVA-Critic-GRPO-dataset@train \
+    data.val_files=lmms-lab/LLaVA-Critic-GRPO-dataset@train \
+    data.shuffle=True \
+    data.format_prompt=./examples/format_prompt/math_format.jinja \
+    data.rollout_batch_size=256 \
+    data.max_prompt_length=4096 \
+    data.prompt_key=question \
+    worker.actor.is_entropy_reg=False \
+    worker.actor.global_batch_size=128 \
+    worker.actor.optim.lr=5.0e-7 \
+    worker.actor.micro_batch_size_per_device_for_update=4 \
+    worker.actor.micro_batch_size_per_device_for_experience=8 \
+    worker.actor.model.model_path=${MODEL_PATH} \
+    worker.rollout.tensor_parallel_size=8 \
+    worker.rollout.limit_images=1 \
+    worker.rollout.gpu_memory_utilization=0.6 \
+    worker.reward.reward_function=./examples/reward_function/math.py:compute_score \
+    trainer.experiment_name=llama3.2v_critic_grpo \
+    trainer.n_gpus_per_node=8 

之前的代码版本/注意力关键词用于sam的/MedEvalKit/LLaVA-NeXT/llava-critic-r1/EasyR1/examples/mimo_vl_7b_llava_critic_grpo.sh

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+#!/bin/bash
+
+MODEL_PATH=XiaomiMiMo/MiMo-VL-7B-SFT-2508
+
+python3 -m verl.trainer.main \
+    config=examples/config.yaml \
+    data.train_files=lmms-lab/LLaVA-Critic-GRPO-dataset@train \
+    data.val_files=lmms-lab/LLaVA-Critic-GRPO-dataset@train \
+    data.filter_overlong_prompts=True \
+    data.prompt_key=question \
+    data.max_prompt_length=6142 \
+    worker.actor.is_entropy_reg=False \
+    worker.actor.global_batch_size=128 \
+    worker.actor.micro_batch_size_per_device_for_update=4 \
+    worker.actor.micro_batch_size_per_device_for_experience=8 \
+    worker.actor.model.model_path=${MODEL_PATH} \
+    trainer.experiment_name=mimo_vl_7b_llava_critic_grpo \
+    trainer.n_gpus_per_node=8 
+

之前的代码版本/注意力关键词用于sam的/MedEvalKit/LLaVA-NeXT/llava-critic-r1/EasyR1/examples/mimo_vl_rl_7b_llava_critic_grpo.sh

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+#!/bin/bash
+
+MODEL_PATH=XiaomiMiMo/MiMo-VL-7B-RL-2508
+
+python3 -m verl.trainer.main \
+    config=examples/config.yaml \
+    data.train_files=lmms-lab/LLaVA-Critic-GRPO-dataset@train \
+    data.val_files=lmms-lab/LLaVA-Critic-GRPO-dataset@train \
+    data.filter_overlong_prompts=True \
+    data.prompt_key=question \
+    data.max_prompt_length=6142 \
+    worker.actor.is_entropy_reg=False \
+    worker.actor.global_batch_size=128 \
+    worker.actor.micro_batch_size_per_device_for_update=4 \
+    worker.actor.micro_batch_size_per_device_for_experience=8 \
+    worker.actor.model.model_path=${MODEL_PATH} \
+    trainer.experiment_name=mimo_vl_rl_7b_llava_critic_grpo \
+    trainer.n_gpus_per_node=8 
+