Skip to main content

Documentation Index

Fetch the complete documentation index at: https://docs.ladderai.website/llms.txt

Use this file to discover all available pages before exploring further.

Ladder is the core training Algorithm of Ladder AI that enables self-improvement of small LLMs through recursive decomposition and guided fine-tuning. It uses a combination of curriculum learning, reinforcement learning, and automated dataset generation to train smaller models without relying on manually curated data or annotations.
llm_engin.py

from ladder.finetuning import Ladder

ladder = Ladder(vladder=Qtrain, config=config,verification_engine=verification_engine, reward_funcs=[]) # add custom reward functions as u need
finetuned_model = ladder.finetune(save_locally=True)

​
Custom Reward Functions

def reward_func(prompts: list[str], completions: list[str], **kwargs) -> list[float]:
    """ Write here your custom reward function that will be used in ladder loop """

​
🔁 Training Loop Overview

The Ladder loop consists of the following key stages:
  1. Problem Decomposition
    The task is recursively broken down into simpler subproblems using the Recursive Tree Engine.
  2. Solution Generation
    The smaller LLM attempts to solve subproblems.
  3. Verification & Evaluation
    A larger LLM or a domain-specific Verification Engine checks the solution quality.
  4. Reward Assignment
    Rewards are computed based on the correctness and usefulness of answers. These guide learning via the GRPO (Guided Reinforcement via Pseudo-Optimization) protocol.
  5. Model Update
    The smaller LLM is fine-tuned using the collected rewards and verified data points.

​
🔍 Role of Engines

Ladder coordinates multiple modular Engines (or agents), each responsible for a specific part of the process:
  • Recursive Tree Engine: Breaks down problems into subproblems
  • Verification Engine: Validates output correctness
  • Difficulty Engine: Modulates complexity
  • LLM Engine: Interfaces with both large and small models
These components work together to ensure high-quality training feedback with minimal manual intervention.