Reasoning methodology improves a conventional language model by eliciting or training explicit intermediate steps before a final answer. This often improves accuracy on multistep tasks, while remaining distinct from rule-based logic. section 1.8
Choose the method according to where computation and supervision are available:
- Use chain-of-thought prompting to increase inference-time computation without retraining the model. section 1.8
- Use reinforcement learning to train explicit reasoning behavior with reward signals. section 1.8
- Use supervised fine-tuning or distillation to transfer reasoning examples from stronger models. section 1.8
- Apply these methods on a pretrained model rather than building the expensive base model from scratch. section 2.3
Contradictory premises
A conventional model may answer that penguins cannot fly after seeing the premise that all birds can fly. It usually relies on probability-weighted patterns, not explicit contradiction checking; unfamiliar scenarios and intricate multistep relationships remain difficult. section 1.5
import torch
print(f"PyTorch version {torch.__version__}")
if torch.cuda.is_available():
print(f"CUDA/ROCm GPU: {torch.cuda.get_device_name(0)}")
elif torch.xpu.is_available():
print(f"Intel GPU: {torch.xpu.get_device_name(0)}")
elif torch.backends.mps.is_available():
print("Apple Silicon GPU")
else:
print("Only CPU")
Check whether the available machine exposes a PyTorch-supported CUDA, ROCm, Intel, Apple Silicon, or CPU execution path before running computationally intensive reasoning work. section 2.3
