Here we assume each example has a 'messages' field Each message is a dict with 'role' and 'content' fields. We concatenate all messages with

Train the model with the given training objective Each training objective is sampled in turn for one batch. We sample only as

Args: observation (`str`): HTML string available_actions (): ... Returns:

Generate from OpenAI Completion API. Args: full_contexts: List of full contexts to generate from. prompt_template: Prompt templat

Generates a redeem code corresponding to the session ID for an MTurk worker once the session is completed

Scrape image from page HTML and return as a list of pixel values

Get mappings of all colors, sizes to corresponding values in COLOR_SET, SIZE_PATTERNS

Compiles state into either the `html` or `text` observation mode

Compiles state into either the `html` or `text` observation mode

Create a new session and reset environment variables

Each scoring example is a dict, which contains the following keys: - prompt: the prompt to score - completions: a list of completions to

Creates a log file and logging object for the corresponding session ID

State that includes all information. The actual observation are likely to be a subset or reduced form of the state.

State that includes all information. The actual observation are likely to be a subset or reduced form of the state.

Takes an action, updates WebShop environment, and returns (observation, reward, done, info) Arguments: action (`str`): An ac

Helper method to strip HTML block of extraneous tags