technique for improving the reasoning capabilities of LLMs by generating intermediate reasoning steps. This helps the LLM generate more accurate answers. You can combine it with few-shot prompting to interpretability with CoT prompting, as you can learn from the LLM’s responses and see the reasoning steps that were followed. If there’s a malfunction, you will be able to identify it. Chain of thought appears volumes of text and math may require a different approach. So let’s see if intermediate reasoning steps will improve the output. Prompt When I was 3 years old, my partner was 3 times my age. Now, I am

Initially, the learning rate linearly increases from 0 to the maximum value during the first 2K steps. Subsequently, the learning rate is multiplied by 0.316 after training about 60% of tokens, and again = 0.0707 ln ? + 1, aiming at minimizing the perplexity. We additionally train the model for 1000 steps, with a sequence length of 32K and a batch size of 576 sequences. Although the training is conducted mathematical and coding abilities of our model can keep improving over a longer period of training steps. Therefore, we employ a two-stage RL training strategy, which first performs reasoning alignment,

Agents are systems that independently accomplish tasks on your behalf. A workflow is a sequence of steps that must be executed to meet the user’s goal, whether that's resolving a customer service issue articles in your knowledge base. Prompt agents to break 
 down tasks Providing smaller, clearer steps from dense resources 
 helps minimize ambiguity and helps the model better 
 follow instructions. anticipates common variations and includes instructions on how to handle them with conditional steps or branches such as an alternative step if a required piece of info is missing. 11 A practical guide

to the upstream ● Improve graph partitioning ● An algorithm to merge supported operators Next Steps Target Device Relay IR Graph Annotation with Your Annotator Graph Partitioning Your Codegen