Add files using upload-large-folder tool

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,73 @@
+---
+license: other
+library_name: transformers
+pipeline_tag: text-generation
+base_model:
+- Qwen/Qwen3-4B-Base
+base_model_relation: finetune
+language:
+- en
+- zh
+tags:
+- safetensors
+- qwen3
+- qwen
+- grpo
+- reinforcement-learning
+- reasoning
+- conversational
+- math
+- arxiv:2604.13016
+---
+
+# Qwen3-4B-Base-GRPO
+
+This repository contains a Qwen3-4B-Base GRPO checkpoint for the collection
+[Rethinking OPD](https://huggingface.co/collections/lllyx/rethinking-opd).
+
+The model is provided in `safetensors` format and can be loaded with
+`transformers`.
+
+## Training Configuration
+
+This checkpoint was trained with the GRPO recipe used in the Rethinking OPD
+experiments.
+
+| Setting | Value |
+| --- | --- |
+| Actor initialization | `model/Qwen3-4B-Base` |
+| Reward/teacher model | `model/Qwen3-4B` |
+| Training data | `datasets/DAPO-Math-17k-Processed/DAPO-Math.parquet` |
+| Validation data | `AIME25`, `AMC23`, `AIME24` |
+| Advantage estimator | `grpo` |
+| GRPO outcome weight | `1.0` |
+| Rollout correction | token-level IS, threshold `2.0` |
+| Prompt length | `1024` |
+| Response length | `7168` |
+| Validation response length | `31744` |
+| Max model length | `32768` |
+| Responses per prompt | `8` |
+| Rollout temperature | `1.0` |
+| Teacher temperature | `1.0` |
+| Repetition penalty | `1.0` |
+| PPO mini-batch size | `64` |
+| Learning rate | `1e-6` |
+| KL loss | disabled |
+| Format reward | disabled |
+| Loss aggregation | `token-mean` |
+| Rollout engine | `vllm` |
+| Tensor parallel size | `1` |
+| GPUs per node | `8` |
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "lllyx/Qwen3-4B-Base-GRPO"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype="auto",
+    device_map="auto",
+)
+```

added_tokens.json

@@ -0,0 +1,28 @@
+{
+  "</think>": 151668,
+  "</tool_call>": 151658,
+  "</tool_response>": 151666,
+  "<think>": 151667,
+  "<tool_call>": 151657,
+  "<tool_response>": 151665,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

chat_template.jinja

@@ -0,0 +1,85 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set content = message.content %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is defined and message.reasoning_content is not none %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in message.content %}
+                {%- set content = message.content.split('</think>')[-1].lstrip('\n') %}
+                {%- set reasoning_content = message.content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if loop.last or (not loop.last and reasoning_content) %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+{%- endif %}

config.json

@@ -0,0 +1,68 @@
+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "eos_token_id": 151643,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2560,
+  "initializer_range": 0.02,
+  "intermediate_size": 9728,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 32768,
+  "max_window_layers": 36,
+  "model_type": "qwen3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 8,
+  "pad_token_id": 151643,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.55.4",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

@@ -0,0 +1,6 @@
+{
+  "bos_token_id": 151643,
+  "eos_token_id": 151643,
+  "max_new_tokens": 2048,
+  "transformers_version": "4.55.4"
+}

grpo.sh

@@ -0,0 +1,230 @@
+#!/bin/bash
+#SBATCH --job-name=url                    # 作业名称
+#SBATCH --output=logs/20251004/output_%j.log            # 标准输出和错误日志文件名 (%j 表示作业ID)
+#SBATCH --error=logs/20251004/error_%j.log              # 错误日志文件名
+#SBATCH --account=test
+#SBATCH --partition=TEST1            # 分区名称
+#SBATCH --exclude=g[81-82]
+#SBATCH --gres=gpu:8                      # 每个节点请求 8 块 GPU
+#SBATCH --ntasks=1                        # 总任务数
+#SBATCH --cpus-per-task=64                # 每个任务分配的CPU核心数
+#SBATCH --mem=500G                       # 分配的内存大小
+#SBATCH --nodes=1                         # 使用 1 个节点
+#SBATCH --ntasks-per-node=1               # 每个节点启动 1 个任务
+
+set -x
+
+# 日志记录设置：如果不是通过SBATCH运行，则自动记录到日志文件
+if [ -z "$SLURM_JOB_ID" ]; then
+    # 本地运行时，创建日志目录和日志文件
+    LOG_DIR=${LOG_DIR:-logs}
+    mkdir -p "$LOG_DIR"
+    LOG_FILE="${LOG_DIR}/run_$(date +%Y%m%d_%H%M%S).log"
+    # 同时输出到终端和日志文件
+    exec > >(tee -a "$LOG_FILE") 2>&1
+    echo "=========================================="
+    echo "日志文件: $LOG_FILE"
+    echo "开始时间: $(date)"
+    echo "=========================================="
+fi
+
+ray stop --force
+export RAY_memory_usage_threshold=0.99
+export CUDA_LAUNCH_BLOCKING=1
+# export CUDA_VISIBLE_DEVICES=1,2,3,4
+export PYTHONUNBUFFERED=1
+export PROJECT_NAME='OnPolicyDistillation' # TODO
+export TORCH_NCCL_BLOCKING_WAIT=1
+export NCCL_TIMEOUT=7200
+export TORCH_DISTRIBUTED_DEBUG=INFO
+# export ADV_ESTIMATOR=token_reward_direct
+# export ADV_ESTIMATOR=token_reward_direct_plus_grpo
+# export ADV_ESTIMATOR=token_grpo
+export ADV_ESTIMATOR=grpo
+export GRPO_OUTCOME_WEIGHT=1.0
+# Swanlab setting used to continue exp  
+# export SWANLAB_RESUME=must
+# export SWANLAB_RUN_ID="jri5qia6iy67v7su0zjsv"
+
+
+export MAX_PROMPT_LENGTH=1024
+export MAX_RESP_LENGTH=7168  # TODO: 31744 /15360 / 7168 / 3072 / 5120
+export MAX_VAL_RESP_LENGTH=31744 # TODO: 15360 / 7168 / 3072
+export MAX_MODEL_LEN=$(( MAX_RESP_LENGTH + MAX_PROMPT_LENGTH > MAX_VAL_RESP_LENGTH + MAX_PROMPT_LENGTH ? MAX_RESP_LENGTH + MAX_PROMPT_LENGTH : MAX_VAL_RESP_LENGTH + MAX_PROMPT_LENGTH ))
+export MINI_BATCH_SIZE=${MINI_BATCH_SIZE:-64} # TODO: 1 / 8 / 16 / 32 / 64 (default 64)
+export TEMPERATURE=${TEMPERATURE:-1.0} # TODO: 0.6 / 0.8 / 1.0 / 1.2 (default 1.0)
+export TEACHER_TEMPERATURE=${TEACHER_TEMPERATURE:-1.0} # Teacher logits temperature (default 1.0, no scaling)
+export REPETITION_PENALTY=${REPETITION_PENALTY:-1.0} # TODO: 1.0 / 1.1 / 1.2 (default 1.0, no penalty)
+export N_RESPONSES=8 # TODO: 4 / 8 / 16 / 32 (default: 8)
+export LOG_PROB_TOP_K=${LOG_PROB_TOP_K:-0} # 0 represents no top-k sampling
+export TOP_K_STRATEGY=${TOP_K_STRATEGY:-"union"} # "only_stu" or "only_tch" or "intersection" or "union" or "union-intersection"
+export REWARD_WEIGHT_MODE=${REWARD_WEIGHT_MODE:-"student_p"} # "student_p" or "teacher_p" or "none"
+# export LR=${LR:-1e-6}
+# export LR_SCHEDULER=${LR_SCHEDULER:-constant}
+export USE_KL=${USE_KL:-False} # TODO: True / False (default False)
+export ENABLE_FORMAT_REWARD=${ENABLE_FORMAT_REWARD:-False} # TODO: True / False (default False)
+export MODEL_DTYPE=${MODEL_DTYPE:-fp32} # actor/ref/critic 的 fsdp_config.model_dtype，可选 fp32 / bfloat16
+export IS_PLOT=${IS_PLOT:-False} # TODO: True / False (default False)
+export LOSS_AGG_MODE=${LOSS_AGG_MODE:-"token-mean"} # TODO: "token-mean" / "seq-mean-token-sum" / "seq-mean-token-mean" / "seq-mean-token-sum-norm" (default "token-mean")
+
+# TODO: qwen3_1p7b_base / qwen3_1p7b / llama31_8b_base / llama31_8b_inst / qwen3_8b_base / qwen3_8b / qwen25_1p5b_base / qwen25_1p5b_inst / qwen25_7b_base / qwen25_7b_inst / qwen25_math_7b_base / qwen25_math_7b_inst / qwen25_math_1p5b_base / qwen25_math_1p5b_inst / distill_r1_1p5b / olmo2_1124_7b_base / olmo2_1124_7b_sft / olmo2_1124_7b_inst / llama32_3b_inst
+# export EXPERIMENT_NAME=grpo_${TASK}_llama31_tulu3_8b_sft_8k-T_${TEMPERATURE}-n_${N_RESPONSES}-kl_${USE_KL}-mbs_${MINI_BATCH_SIZE}-${REWARD_TYPE}-$(date +%Y-%m-%d_%H-%M-%S)
+
+# export TRAIN_DATASET=datasets/DeepMath-103K/verl_format/train.parquet
+# export TRAIN_DATASET=datasets/DAPO-Math-17k/data/dapo-math-17k-1percent.parquet
+# export TRAIN_DATASET=datasets/DAPO-Math-17k/data/dapo-math-17k-1percent-processed.parquet
+export TRAIN_DATASET=datasets/DAPO-Math-17k-Processed/DAPO-Math.parquet
+# export TRAIN_DATASET=datasets/DeepMath-103K/verl_format/sampled_5k.parquet
+# export TRAIN_DATASET=datasets/OpenThoughts3-1.2M/verl_format/train.parquet
+export TRAIN_DATASET_NAME=DAPO-Math-17k-Processed
+# export TRAIN_DATASET_NAME=DeepMath-103K-sampled_5k
+# export TRAIN_DATASET_NAME=DeepMath-103K
+
+export TEST_DATA_DIR=datasets/test_data
+# TRAIN_DATASET=${TRAIN_FILE:-["$DATA_DIR/$TASK/train_${SAMPLE_SIZE}.parquet"]}
+TEST_DATASET=${TEST_FILE:-["$TEST_DATA_DIR/AIME25/test.parquet", "$TEST_DATA_DIR/AMC23/test.parquet", "$TEST_DATA_DIR/AIME24/test.parquet"]}
+# TEST_DATASET=${TEST_FILE:-["$TEST_DATA_DIR/AIME24/test.parquet"]}
+# TEST_DATASET=${TEST_FILE:-["$DATA_DIR/AIME24/test.parquet","$DATA_DIR/AIME25/test.parquet","$DATA_DIR/AMC23/test.parquet","$DATA_DIR/MATH-500/test.parquet","$DATA_DIR/Minerva/test.parquet","$DATA_DIR/Olympiad-Bench/test.parquet"]}
+
+# TODO:
+# export ACTOR_MODEL_PATH=model/qwen3-1.7b-math-sft
+# export ACTOR_MODEL_PATH=model/DeepSeek-R1-Distill-Qwen-1.5B
+# export ACTOR_MODEL_PATH=model/Qwen3-1.7B-Base
+# export ACTOR_MODEL_PATH=model/Qwen3-1.7B
+# export ACTOR_MODEL_PATH=model/Qwen3-1.7B-sft/checkpoint-6000
+# export ACTOR_MODEL_PATH=model/DeepSeek-R1-Distill-Qwen-7B
+# export ACTOR_MODEL_PATH=model/DS-1.5B-SFT
+export ACTOR_MODEL_PATH=model/Qwen3-4B-Base
+# export ACTOR_MODEL_NAME=model/Qwen3-4B-grpo
+# export ACTOR_MODEL_NAME=model/Qwen3-4B
+export ACTOR_MODEL_NAME=$(basename "$ACTOR_MODEL_PATH")
+export REWARD_MODEL_PATH=model/Qwen3-4B
+# export REWARD_MODEL_PATH=model/Qwen3-1.7B
+# export REWARD_MODEL_PATH=model/DeepSeek-R1-Distill-Qwen-7B
+# export REWARD_MODEL_PATH=model/Skywork-OR1-Math-7B
+# export REWARD_MODEL_PATH=model/DeepSeek-R1-Distill-Qwen-14B
+# export REWARD_MODEL_PATH=model/JustRL-DeepSeek-1.5B
+export REWARD_MODEL_NAME=$(basename "$REWARD_MODEL_PATH")
+
+export PROJECT_PATH=checkpoint
+export PARALLEL_SIZE=1
+export CKPT_PATH=${PROJECT_PATH}/${ADV_ESTIMATOR}_${TRAIN_DATASET_NAME}_${ACTOR_MODEL_NAME}_${REWARD_MODEL_NAME}_${MAX_RESP_LENGTH}-T_${TEMPERATURE}-Tch_${TEACHER_TEMPERATURE}-n_${N_RESPONSES}-mbs_${MINI_BATCH_SIZE}-topk_${LOG_PROB_TOP_K}-topk_strategy_${TOP_K_STRATEGY}-rw_${REWARD_WEIGHT_MODE}-$(date +%Y-%m-%d_%H-%M-%S)
+export OUTLINES_CACHE_DIR=~/.cache/outlines/$(uuidgen)
+export NCCL_DEBUG=WARN
+
+# export VLLM_ATTENTION_BACKEND=XFORMERS
+# export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
+export TOKENIZERS_PARALLELISM=true
+export SWANLAB_LOG_DIR=${PROJECT_PATH}/swanlab_log
+export HYDRA_FULL_ERROR=1
+
+
+export EXPERIMENT_NAME=${ADV_ESTIMATOR}_${TRAIN_DATASET_NAME}_${ACTOR_MODEL_NAME}_${REWARD_MODEL_NAME}_${MAX_RESP_LENGTH}-T_${TEMPERATURE}-Tch_${TEACHER_TEMPERATURE}-n_${N_RESPONSES}-mbs_${MINI_BATCH_SIZE}-topk_${LOG_PROB_TOP_K}-topk_strategy_${TOP_K_STRATEGY}-rw_${REWARD_WEIGHT_MODE}-$(date +%Y-%m-%d_%H-%M-%S)
+
+KL_ARGS=""
+if [ "$USE_KL" = "True" ]; then
+    KL_ARGS="actor_rollout_ref.actor.use_kl_loss=True \
+    actor_rollout_ref.actor.kl_loss_coef=0.005 \
+    actor_rollout_ref.actor.kl_loss_type=low_var_kl"
+else
+    KL_ARGS="actor_rollout_ref.actor.use_kl_loss=False"
+fi
+
+LR_ARGS=""
+if [ "$LR_SCHEDULER" = "cosine" ]; then
+    LR_ARGS="actor_rollout_ref.actor.optim.warmup_style=cosine \
+    actor_rollout_ref.actor.optim.lr_warmup_steps_ratio=0.03"
+fi
+
+PPO_MAX_TOKEN_LEN_PER_GPU=$(( ((1024 + MAX_RESP_LENGTH) > 32768) ? (1024 + MAX_RESP_LENGTH) : 32768))
+echo "PPO_MAX_TOKEN_LEN_PER_GPU: $PPO_MAX_TOKEN_LEN_PER_GPU"
+
+
+ray start --head
+sleep 5
+
+
+python3 -m verl.trainer.main_ppo \
+    algorithm.adv_estimator=$ADV_ESTIMATOR \
+    algorithm.grpo_outcome_weight=$GRPO_OUTCOME_WEIGHT \
+    +algorithm.rollout_correction.rollout_is=token \
+    +algorithm.rollout_correction.rollout_is_threshold=2.0 \
+    data.shuffle=False \
+    data.train_files="$TRAIN_DATASET" \
+    data.val_files="$TEST_DATASET" \
+    data.train_batch_size=$((${MINI_BATCH_SIZE}*${PARALLEL_SIZE})) \
+    data.max_prompt_length=$MAX_PROMPT_LENGTH \
+    data.max_response_length=$MAX_RESP_LENGTH \
+    data.filter_overlong_prompts=True \
+    data.truncation='error' \
+    data.return_raw_chat=True \
+    actor_rollout_ref.model.path=$ACTOR_MODEL_PATH \
+    actor_rollout_ref.model.use_remove_padding=True \
+    actor_rollout_ref.model.enable_activation_offload=True \
+    actor_rollout_ref.model.enable_gradient_checkpointing=True \
+    actor_rollout_ref.actor.optim.lr=1e-6 \
+    $LR_ARGS \
+    actor_rollout_ref.actor.ppo_mini_batch_size=$MINI_BATCH_SIZE \
+    actor_rollout_ref.actor.use_dynamic_bsz=True \
+    actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=1 \
+    actor_rollout_ref.actor.ppo_max_token_len_per_gpu=$PPO_MAX_TOKEN_LEN_PER_GPU \
+    actor_rollout_ref.actor.ulysses_sequence_parallel_size=$PARALLEL_SIZE \
+    $KL_ARGS \
+    actor_rollout_ref.actor.loss_agg_mode=$LOSS_AGG_MODE \
+    actor_rollout_ref.actor.fsdp_config.param_offload=False \
+    actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \
+    actor_rollout_ref.actor.fsdp_config.forward_prefetch=True \
+    actor_rollout_ref.actor.fsdp_config.model_dtype=$MODEL_DTYPE \
+    actor_rollout_ref.rollout.max_num_batched_tokens=$PPO_MAX_TOKEN_LEN_PER_GPU \
+    actor_rollout_ref.ref.fsdp_config.param_offload=True \
+    actor_rollout_ref.ref.fsdp_config.model_dtype=$MODEL_DTYPE \
+    actor_rollout_ref.ref.log_prob_use_dynamic_bsz=True \
+    actor_rollout_ref.rollout.name=vllm \
+    actor_rollout_ref.rollout.temperature=$TEMPERATURE \
+    actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=True \
+    +actor_rollout_ref.rollout.log_prob_top_k=$LOG_PROB_TOP_K \
+    +actor_rollout_ref.rollout.top_k_strategy=$TOP_K_STRATEGY \
+    +actor_rollout_ref.rollout.reward_weight_mode=$REWARD_WEIGHT_MODE \
+    +actor_rollout_ref.rollout.teacher_temperature=$TEACHER_TEMPERATURE \
+    actor_rollout_ref.rollout.tensor_model_parallel_size=$PARALLEL_SIZE \
+    actor_rollout_ref.rollout.gpu_memory_utilization=0.8 \
+    actor_rollout_ref.rollout.max_model_len=$MAX_MODEL_LEN \
+    actor_rollout_ref.rollout.n=$N_RESPONSES \
+    actor_rollout_ref.rollout.val_kwargs.do_sample=True \
+    +actor_rollout_ref.rollout.val_kwargs.max_tokens=$MAX_VAL_RESP_LENGTH \
+    actor_rollout_ref.rollout.val_kwargs.n=16 \
+    actor_rollout_ref.rollout.val_kwargs.temperature=1.0 \
+    actor_rollout_ref.rollout.val_kwargs.top_p=0.95 \
+    actor_rollout_ref.rollout.repetition_penalty=$REPETITION_PENALTY \
+    actor_rollout_ref.rollout.calculate_log_probs=True \
+    actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=1 \
+    reward_model.enable=False \
+    +reward_model.reward_kwargs.enable_format_reward=$ENABLE_FORMAT_REWARD \
+    reward_model.model.path=$REWARD_MODEL_PATH \
+    reward_model.model.input_tokenizer=null \
+    reward_model.model.use_remove_padding=True \
+    reward_model.model.fsdp_config.param_offload=False \
+    +reward_model.model.dtype=$MODEL_DTYPE \
+    reward_model.micro_batch_size_per_gpu=24 \
+    custom_reward_function.path="verl/verl/utils/reward_score/ttrl_math/__init__.py" \
+    custom_reward_function.name=reward_func \
+    trainer.val_before_train=False \
+    trainer.log_val_generations=2 \
+    trainer.logger=['console','swanlab'] \
+    trainer.project_name=$PROJECT_NAME \
+    trainer.experiment_name=$EXPERIMENT_NAME \
+    trainer.validation_data_dir=validation_log/$EXPERIMENT_NAME \
+    trainer.n_gpus_per_node=8 \
+    trainer.nnodes=1 \
+    trainer.save_freq=20 \
+    trainer.test_freq=20 \
+    trainer.total_epochs=1 \
+    trainer.default_local_dir="$CKPT_PATH" \
+    trainer.is_plot=$IS_PLOT \
+
+# 记录结束信息
+if [ -z "$SLURM_JOB_ID" ]; then
+    echo "=========================================="
+    echo "结束时间: $(date)"
+    echo "=========================================="
+fi

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+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
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+}