google-tunix

from tunix import AgenticGRPOConfig # Configure Agentic GRPO for LLM post-training # This is a minimal configuration; a real setup would require more specific parameters # like model_config, optimizers, and potentially a tokenizer. agentic_grpo_config = AgenticGRPOConfig( num_generations=2, # Number of generations per iteration num_iterations=10, # Total training iterations max_response_length=512, # Maximum length for generated responses beta=0.1, # KL-divergence coefficient # Placeholders for complex objects; in a real scenario these would be actual config objects model_config=None, # e.g., Llama2Config, GemmaConfig optimizer_config_factory=lambda: None, # Factory for optimizer configs ) print(f"AgenticGRPOConfig initialized with num_generations: {agentic_grpo_config.num_generations}") print(f"Max response length: {agentic_grpo_config.max_response_length}") # Note: To run a full training loop, you would also need to instantiate # AgenticGRPOLearner with actual JAX/Flax models, a tokenizer, and a dataset.