what is trl-token-reduction fr?

This project is a research tool that sits between your application and an expensive AI model like Claude or GPT, with one goal: cut how many tokens you pay for without hurting the quality of the answers. Tokens are the small chunks of text that AI providers charge for, and the README explains that AI agents are often wasteful, resending the same system instructions, tool descriptions, and full conversation history on every single step, and dumping huge raw files into the AI's context even when only a small part is relevant. The project offers four separate techniques that can be used together. The first is caching, which charges the stable, unchanging parts of a prompt once instead of every time. The second is compression, where a smaller local model summarizes and removes repeated information from a growing conversation, paired with a safety check that puts back any specific number the summary accidentally dropped. The third is retrieval, which replaces sending whole code files with just the small, relevant pieces of code that were actually needed, found using a code-parsing tool called tree-sitter that runs locally without using any API tokens. The fourth is a cascade approach, where simple or verifiable steps get answered by a cheap local process and only genuinely hard steps get sent to the expensive frontier model. The README backs its claims with real, reproducible benchmark numbers rather than vague statements, including tests run against actual paid API calls to GPT-5.5 and Claude Opus 4.8, where token usage on realistic tasks dropped by roughly 87 to 96 percent while answer quality stayed equal to the unreduced baseline. It also describes a real, months-long usage log from a full Roblox game project built with an AI coding assistant, where the retrieval technique alone saved hundreds of thousands of tokens as the project's codebase grew. The author is upfront that this is a research-stage build, that some benchmark scenarios use favorable or synthetic workloads, and that real-world savings will vary depending on how much of a given task is routine versus genuinely novel.