config_demo_code_ioi.yaml

# Model arguments
model_name_or_path: Qwen/Qwen2.5-1.5B-Instruct
model_revision: main
torch_dtype: bfloat16
attn_implementation: flash_attention_2

# Data training arguments
dataset_name: open-r1/ioi
system_prompt: "You are a helpful AI Assistant that provides well-reasoned and detailed responses. You first think about the reasoning process as an internal monologue and then provide the user with the answer. Respond in the following format: <think>\n...\n</think>\n<answer>\n...\n</answer>"

# GRPO trainer config
beta: 0.01
bf16: true
use_vllm: true
vllm_device: auto
vllm_gpu_memory_utilization: 0.9
do_eval: false
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
hub_model_id: Qwen2.5-1.5B-Open-R1-Code-GRPO
hub_strategy: every_save
learning_rate: 5.0e-06
log_completions: true
log_level: info
logging_first_step: true
logging_steps: 1
logging_strategy: steps
lr_scheduler_type: cosine_with_min_lr
lr_scheduler_kwargs:
  min_lr_rate: 0.1
max_prompt_length: 1024
max_completion_length: 2048
max_steps: 500
num_generations: 14
num_train_epochs: 1
output_dir: data/Qwen2.5-1.5B-Open-R1-Code-GRPO
overwrite_output_dir: true
per_device_train_batch_size: 16
push_to_hub: true
report_to:
- wandb
save_strategy: "steps"
save_steps: 50
save_total_limit: 1
seed: 42
temperature: 1.0
warmup_ratio: 0.03
# ioi specific config
code_language: cpp
reward_funcs:
- ioi_code
- code_format
- format
reward_weights:
- 1.0
- 0.1
- 0.1
# for each generation, evaluate these many test cases in parallel, then check if any of them failed (0 score): if so stop evaluating
# otherwise continue with the next batch of test cases. Useful to avoid overloading the eval server + save time on wrong solutions
code_eval_test_batch_size: 3