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README.md

@@ -1,3 +1,68 @@
----
-license: mit
----
+---
+license: mit
+library_name: transformers
+datasets:
+- AI-MO/NuminaMath-CoT
+- KbsdJames/Omni-MATH
+- RUC-AIBOX/STILL-3-Preview-RL-Data
+- hendrycks/competition_math
+language:
+- en
+base_model:
+- deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
+pipeline_tag: text-generation
+---
+
+<span style="font-family: default; font-size: 1.5em;">DeepScaleR-1.5B-Preview-Reproduce</span>
+
+## Overview
+This model is a reproduction of the [agentica-project/deepscaler](https://github.com/agentica-project/deepscaler) project. We have reproduced the results in the repo on an **8x80G A800**, achieving an average score of **56.4**.
+
+
+## Training
+```
+export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+export VLLM_ATTENTION_BACKEND=XFORMERS
+
+# Run 8K context length training, 560 steps
+export MODEL_PATH="deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"
+nohup bash run_deepscaler_1.5b_8k.sh --model $MODEL_PATH > stage1.log 2>&1 &
+
+# Run 16K context length training, 250 steps
+export MODEL_PATH="./checkpoints/deepscaler/deepscaler-1.5b-8k/actor/global_step_560"
+nohup bash run_deepscaler_1.5b_16k.sh --model $MODEL_PATH > stage2.log 2>&1 &
+
+# Run 24K context length training, 190 steps
+export MODEL_PATH="./checkpoints/deepscaler/deepscaler-1.5b-16k/actor/global_step_250"
+nohup bash run_deepscaler_1.5b_24k.sh --model $MODEL_PATH > stage3.log 2>&1 &
+
+# Run 24K context length training, 480 steps
+export MODEL_PATH="./checkpoints/deepscaler/deepscaler-1.5b-24k/actor/global_step_190"
+nohup bash run_deepscaler_1.5b_24k.sh --model $MODEL_PATH > stage3-continue.log 2>&1 &
+```
+
+## Evaluation
+We report Pass@1 accuracy averaged over 16 samples for each problem.
+| Model | AIME 2024 | MATH 500 | AMC 2023 | Minerva Math | OlympiadBench | Avg. |
+|-------|-----------|-----------|-----------|--------------|---------------|------|
+| Qwen-2.5-7B-Instruct | 13.3 | 79.8 | 50.6 | 34.6 | 40.7 | 43.8 |
+| rStar-Math-7B | 26.7 | 78.4 | 47.5 | - | 47.1 | - |
+| Eurus-2-7B-PRIME | 26.7 | 79.2 | 57.8 | 38.6 | 42.1 | 48.9 |
+| Qwen2.5-7B-SimpleRL | 26.7 | 82.4 | 62.5 | <strong>39.7</strong> | 43.3 | 50.9 |
+| DeepSeek-R1-Distill-Qwen-1.5B | 28.8 | 82.8 | 62.9 | 26.5 | 43.3 | 48.9 |
+| Still-1.5B | 32.5 | 84.4 | 66.7 | 29.0 | 45.4 | 51.6 |
+| <strong>DeepScaleR-1.5B-Preview</strong> | <strong>43.1</strong> | <strong>87.8</strong> | <strong>73.6</strong> | 30.2 | <strong>50.0</strong> | <strong>57.0</strong> |
+| <strong>DeepScaleR-1.5B-Preview-Reproduce</strong> | <strong>40.4</strong> | <strong>87.9</strong> | <strong>72.0</strong> | 31.5 | <strong>50.2</strong> | <strong>56.4</strong> |
+| O1-Preview | 40.0 | 81.4 | - | - | - | - |
+
+
+## Citation 
+```bibtex
+@misc{deepscaler2025,
+  title={DeepScaleR: Surpassing O1-Preview with a 1.5B Model by Scaling RL},
+  author={Michael Luo and Sijun Tan and Justin Wong and Xiaoxiang Shi and William Y. Tang and Manan Roongta and Colin Cai and Jeffrey Luo and Tianjun Zhang and Li Erran Li and Raluca Ada Popa and Ion Stoica},
+  year={2025},
+  howpublished={\url{https://pretty-radio-b75.notion.site/DeepScaleR-Surpassing-O1-Preview-with-a-1-5B-Model-by-Scaling-RL-19681902c1468005bed8ca303013a4e2}},
+  note={Notion Blog}
+  year={2025}
+}

config.json

@@ -0,0 +1,31 @@
+{
+  "_name_or_path": "deepscaler/deepscaler-1.5b-24k/actor/global_step_190",
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151646,
+  "eos_token_id": 151643,
+  "hidden_act": "silu",
+  "hidden_size": 1536,
+  "initializer_range": 0.02,
+  "intermediate_size": 8960,
+  "max_position_embeddings": 131072,
+  "max_window_layers": 21,
+  "model_type": "qwen2",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 2,
+  "pad_token_id": 151643,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 10000,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.47.1",
+  "use_cache": true,
+  "use_mrope": false,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 151646,
+  "do_sample": true,
+  "eos_token_id": 151643,
+  "temperature": 0.6,
+  "top_p": 0.95,
+  "transformers_version": "4.47.1"
+}

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run_deepscaler_1.5b_16k.sh

@@ -0,0 +1,69 @@
+#!/bin/bash
+set -x
+
+# Warning: Export VLLM_ATTENTION_BACKEND on every machine before starting Ray cluster.
+# vLLM without XFORMERS will results in CUDA errors.
+export VLLM_ATTENTION_BACKEND=XFORMERS
+
+# Parse command line arguments
+while [[ $# -gt 0 ]]; do
+    case $1 in
+        --model)
+            MODEL_PATH="$2"
+            shift 2
+            ;;
+        *)
+            break
+            ;;
+    esac
+done
+
+# Set default model path if not provided
+if [ -z "$MODEL_PATH" ]; then
+    MODEL_PATH="deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"
+fi
+
+# Train over 4 nodes, 8 A100-80GB GPUs per node.
+python3 -m verl.trainer.main_ppo \
+    algorithm.adv_estimator=grpo \
+    data.train_files=$HOME/deepscaler/data/train.parquet \
+    data.val_files=$HOME/deepscaler/data/aime.parquet \
+    data.train_batch_size=64 \
+    data.val_batch_size=256 \
+    data.max_prompt_length=1024 \
+    data.max_response_length=16384 \
+    actor_rollout_ref.model.path=$MODEL_PATH \
+    actor_rollout_ref.actor.optim.lr=1e-6 \
+    actor_rollout_ref.model.use_remove_padding=True \
+    actor_rollout_ref.actor.ppo_mini_batch_size=64 \
+    actor_rollout_ref.actor.ppo_epochs=1 \
+    actor_rollout_ref.actor.use_dynamic_bsz=True \
+    actor_rollout_ref.actor.ppo_max_token_len_per_gpu=32768 \
+    actor_rollout_ref.actor.use_kl_loss=True \
+    actor_rollout_ref.actor.kl_loss_coef=0.001 \
+    actor_rollout_ref.actor.kl_loss_type=low_var_kl \
+    actor_rollout_ref.actor.ulysses_sequence_parallel_size=1 \
+    actor_rollout_ref.model.enable_gradient_checkpointing=True \
+    actor_rollout_ref.actor.fsdp_config.param_offload=False \
+    actor_rollout_ref.actor.fsdp_config.grad_offload=False \
+    actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \
+    actor_rollout_ref.rollout.tensor_model_parallel_size=1 \
+    actor_rollout_ref.rollout.name=vllm \
+    actor_rollout_ref.rollout.temperature=0.6 \
+    actor_rollout_ref.rollout.val_temperature=0.6 \
+    actor_rollout_ref.rollout.gpu_memory_utilization=0.85 \
+    actor_rollout_ref.rollout.n=16 \
+    actor_rollout_ref.rollout.n_val=16 \
+    actor_rollout_ref.ref.fsdp_config.param_offload=True \
+    algorithm.kl_ctrl.kl_coef=0.001 \
+    trainer.critic_warmup=0 \
+    trainer.logger=['wandb'] \
+    trainer.project_name='deepscaler' \
+    trainer.experiment_name='deepscaler-1.5b-16k' \
+    +trainer.val_before_train=True \
+    trainer.n_gpus_per_node=8 \
+    trainer.nnodes=1 \
+    trainer.save_freq=10 \
+    trainer.test_freq=10 \
+    trainer.default_hdfs_dir=null \
+    trainer.total_epochs=30 "${@:1}"

run_deepscaler_1.5b_24k.sh

@@ -0,0 +1,69 @@
+#!/bin/bash
+set -x
+
+# Warning: Export VLLM_ATTENTION_BACKEND on every machine before starting Ray cluster.
+# vLLM without XFORMERS will results in CUDA errors.
+export VLLM_ATTENTION_BACKEND=XFORMERS
+
+# Parse command line arguments
+while [[ $# -gt 0 ]]; do
+    case $1 in
+        --model)
+            MODEL_PATH="$2"
+            shift 2
+            ;;
+        *)
+            break
+            ;;
+    esac
+done
+
+# Check if model path is provided
+if [ -z "$MODEL_PATH" ]; then
+    MODEL_PATH="deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"
+fi
+
+# Train over 4 nodes, 8 A100-80GB GPUs per node.
+python3 -m verl.trainer.main_ppo \
+    algorithm.adv_estimator=grpo \
+    data.train_files=$HOME/deepscaler/data/train.parquet \
+    data.val_files=$HOME/deepscaler/data/aime.parquet \
+    data.train_batch_size=64 \
+    data.val_batch_size=128 \
+    data.max_prompt_length=1024 \
+    data.max_response_length=24576 \
+    actor_rollout_ref.model.path=$MODEL_PATH \
+    actor_rollout_ref.actor.optim.lr=1e-6 \
+    actor_rollout_ref.model.use_remove_padding=True \
+    actor_rollout_ref.actor.ppo_mini_batch_size=64 \
+    actor_rollout_ref.actor.ppo_epochs=1 \
+    actor_rollout_ref.actor.use_dynamic_bsz=True \
+    actor_rollout_ref.actor.ppo_max_token_len_per_gpu=32768 \
+    actor_rollout_ref.actor.use_kl_loss=True \
+    actor_rollout_ref.actor.kl_loss_coef=0.001 \
+    actor_rollout_ref.actor.kl_loss_type=low_var_kl \
+    actor_rollout_ref.actor.ulysses_sequence_parallel_size=1 \
+    actor_rollout_ref.model.enable_gradient_checkpointing=True \
+    actor_rollout_ref.actor.fsdp_config.param_offload=False \
+    actor_rollout_ref.actor.fsdp_config.grad_offload=False \
+    actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \
+    actor_rollout_ref.rollout.tensor_model_parallel_size=1 \
+    actor_rollout_ref.rollout.name=vllm \
+    actor_rollout_ref.rollout.temperature=0.6 \
+    actor_rollout_ref.rollout.val_temperature=0.6 \
+    actor_rollout_ref.rollout.gpu_memory_utilization=0.7 \
+    actor_rollout_ref.rollout.n=16 \
+    actor_rollout_ref.rollout.n_val=16 \
+    actor_rollout_ref.ref.fsdp_config.param_offload=True \
+    algorithm.kl_ctrl.kl_coef=0.001 \
+    trainer.critic_warmup=0 \
+    trainer.logger=['wandb'] \
+    trainer.project_name='deepscaler' \
+    trainer.experiment_name='deepscaler-1.5b-24k' \
+    +trainer.val_before_train=False \
+    trainer.n_gpus_per_node=8 \
+    trainer.nnodes=1 \
+    trainer.save_freq=10 \
+    trainer.test_freq=10 \
+    trainer.default_hdfs_dir=null \
+    trainer.total_epochs=30 "${@:1}"

run_deepscaler_1.5b_8k.sh

@@ -0,0 +1,68 @@
+#!/bin/bash
+set -x
+
+# Warning: Export VLLM_ATTENTION_BACKEND on every machine before starting Ray cluster.
+# vLLM without XFORMERS will results in CUDA errors.
+export VLLM_ATTENTION_BACKEND=XFORMERS
+
+# Parse command line arguments
+while [[ $# -gt 0 ]]; do
+    case $1 in
+        --model)
+            MODEL_PATH="$2"
+            shift 2
+            ;;
+        *)
+            break
+            ;;
+    esac
+done
+
+# Set default model path if not provided
+if [ -z "$MODEL_PATH" ]; then
+    MODEL_PATH="deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"
+fi
+
+# Train over a single node, 8 A100-80GB GPUs.
+python3 -m verl.trainer.main_ppo \
+    algorithm.adv_estimator=grpo \
+    data.train_files=$HOME/deepscaler/data/train.parquet \
+    data.val_files=$HOME/deepscaler/data/aime.parquet \
+    data.train_batch_size=128 \
+    data.val_batch_size=512 \
+    data.max_prompt_length=1024 \
+    data.max_response_length=8192 \
+    actor_rollout_ref.model.path=$MODEL_PATH  \
+    actor_rollout_ref.actor.optim.lr=1e-6 \
+    actor_rollout_ref.model.use_remove_padding=True \
+    actor_rollout_ref.actor.ppo_mini_batch_size=64 \
+    actor_rollout_ref.actor.use_dynamic_bsz=True \
+    actor_rollout_ref.actor.ppo_max_token_len_per_gpu=32768 \
+    actor_rollout_ref.actor.use_kl_loss=True \
+    actor_rollout_ref.actor.kl_loss_coef=0.001 \
+    actor_rollout_ref.actor.kl_loss_type=low_var_kl \
+    actor_rollout_ref.actor.ulysses_sequence_parallel_size=1 \
+    actor_rollout_ref.model.enable_gradient_checkpointing=True \
+    actor_rollout_ref.actor.fsdp_config.param_offload=False \
+    actor_rollout_ref.actor.fsdp_config.grad_offload=False \
+    actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \
+    actor_rollout_ref.rollout.tensor_model_parallel_size=1 \
+    actor_rollout_ref.rollout.name=vllm \
+    actor_rollout_ref.rollout.temperature=0.6 \
+    actor_rollout_ref.rollout.val_temperature=0.6 \
+    actor_rollout_ref.rollout.gpu_memory_utilization=0.85 \
+    actor_rollout_ref.rollout.n=8 \
+    actor_rollout_ref.rollout.n_val=8 \
+    actor_rollout_ref.ref.fsdp_config.param_offload=True \
+    algorithm.kl_ctrl.kl_coef=0.001 \
+    trainer.critic_warmup=0 \
+    trainer.logger=['wandb'] \
+    trainer.project_name='deepscaler' \
+    trainer.experiment_name='deepscaler-1.5b-8k' \
+    +trainer.val_before_train=True \
+    trainer.n_gpus_per_node=8 \
+    trainer.nnodes=1 \
+    trainer.save_freq=20 \
+    trainer.test_freq=20 \
+    trainer.default_hdfs_dir=null \
+    trainer.total_epochs=30 "${@:1}"

special_tokens_map.json

@@ -0,0 +1,23 @@
+{
+  "bos_token": {
+    "content": "<｜begin▁of▁sentence｜>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<｜end▁of▁sentence｜>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<｜end▁of▁sentence｜>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e20ddafc659ba90242154b55275402edeca0715e5dbb30f56815a4ce081f4893
+size 11422778

tokenizer_config.json

@@ -0,0 +1,195 @@
+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "add_prefix_space": null,
+  "added_tokens_decoder": {
+    "151643": {
+      "content": "<｜end▁of▁sentence｜>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151644": {
+      "content": "<｜User｜>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151645": {
+      "content": "<｜Assistant｜>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151646": {
+      "content": "<｜begin▁of▁sentence｜>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151647": {
+      "content": "<|EOT|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151648": {
+      "content": "<think>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151649": {
+      "content": "</think>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151650": {
+      "content": "<|quad_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151651": {
+      "content": "<|quad_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151652": {
+      "content": "<|vision_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151653": {
+      "content": "<|vision_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151654": {
+      "content": "<|vision_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151655": {
+      "content": "<|image_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151656": {
+      "content": "<|video_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151657": {
+      "content": "<tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151658": {
+      "content": "</tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151659": {
+      "content": "<|fim_prefix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151660": {
+      "content": "<|fim_middle|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151661": {
+      "content": "<|fim_suffix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151662": {
+      "content": "<|fim_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151663": {
+      "content": "<|repo_name|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151664": {
+      "content": "<|file_sep|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "bos_token": "<｜begin▁of▁sentence｜>",
+  "chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set ns = namespace(is_first=false, is_tool=false, is_output_first=true, system_prompt='') %}{%- for message in messages %}{%- if message['role'] == 'system' %}{% set ns.system_prompt = message['content'] %}{%- endif %}{%- endfor %}{{bos_token}}{{ns.system_prompt}}{%- for message in messages %}{%- if message['role'] == 'user' %}{%- set ns.is_tool = false -%}{{'<｜User｜>' + message['content']}}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is none %}{%- set ns.is_tool = false -%}{%- for tool in message['tool_calls']%}{%- if not ns.is_first %}{{'<｜Assistant｜><｜tool▁calls▁begin｜><｜tool▁call▁begin��>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\\n' + '```json' + '\\n' + tool['function']['arguments'] + '\\n' + '```' + '<｜tool▁call▁end｜>'}}{%- set ns.is_first = true -%}{%- else %}{{'\\n' + '<｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\\n' + '```json' + '\\n' + tool['function']['arguments'] + '\\n' + '```' + '<｜tool▁call▁end｜>'}}{{'<｜tool▁calls▁end｜><｜end▁of▁sentence｜>'}}{%- endif %}{%- endfor %}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is not none %}{%- if ns.is_tool %}{{'<｜tool▁outputs▁end｜>' + message['content'] + '<｜end▁of▁sentence｜>'}}{%- set ns.is_tool = false -%}{%- else %}{% set content = message['content'] %}{% if '</think>' in content %}{% set content = content.split('</think>')[-1] %}{% endif %}{{'<｜Assistant｜>' + content + '<｜end▁of▁sentence｜>'}}{%- endif %}{%- endif %}{%- if message['role'] == 'tool' %}{%- set ns.is_tool = true -%}{%- if ns.is_output_first %}{{'<｜tool▁outputs▁begin｜><｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}{%- set ns.is_output_first = false %}{%- else %}{{'\\n<｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}{%- endif %}{%- endif %}{%- endfor -%}{% if ns.is_tool %}{{'<｜tool▁outputs▁end｜>'}}{% endif %}{% if add_generation_prompt and not ns.is_tool %}{{'<｜Assistant｜>'}}{% endif %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<｜end▁of▁sentence｜>",
+  "extra_special_tokens": {},
+  "legacy": true,
+  "model_max_length": 16384,
+  "pad_token": "<｜end▁of▁sentence｜>",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": null,
+  "use_default_system_prompt": false
+}