Recursive Language Model

A Recursive Language Model, after Prime Intellect's RLM, built as an instance tree. The root model never reads the input. The input sits in a persistent Python REPL on the agent's machine, as context, and the model works on it with code through one tool, python.

Code in the REPL can call sub-LLMs: llm_batch(prompts) runs them in parallel, and llm_query(prompt) runs one. The model answers by setting answer["content"] and then answer["ready"] = True. It sees what a cell prints, capped at 8192 characters.

• rlm.tsx
  • RLM: the agent.
  • Direct: the baseline, which reads the whole context in its prompt.
  • The default export, Bench: mounts both per task and grades their answers into @results.
• tools/examples/rlm/rlm_repl (development checkout only): the REPL, a daemon per machine home (start FILE, exec, resume, stop). It is an optional Python helper, excluded from the framework package, and makes no model calls.
• tools/fixtures/make_rlm_tasks.py: Oolong-style aggregation tasks. Each is a log of user messages whose category (complaint, praise, question, request) is never written down, and each question aggregates over those categories (how many complaints, which user praised most, how many questions from one user). Answering means reading every line, not finding one.

#How sub-LLM calls work

llm_batch parks the running cell, and the exec call returns the prompts. The python handler then:

1. answers every prompt at once with Promise.all over model(<Sub prompt=.../>);
2. sends the replies back with resume;
3. loops until the cell finishes.

So sub-LLM calls are the instance's own model calls. They are in its journal and under its budget, and on resume they replay instead of being called again. The REPL never sees a key or a model endpoint.

#Run

python3 tools/fixtures/make_rlm_tasks.py 11 3000 > tasks.json   # 3,000 messages, ~307K chars

Its deployment is affordance.yaml: put the tasks in root.props.tasks, set the provider and export its key as API_KEY, then affordance run views/demo/rlm.

env.PATH must be absolute, because a local machine runs commands in the agent's home. The results are in .affordance/root/snapshot.json (state.__ui["@results"]). The REPL daemons outlive the run, since they belong to the machines; stop them with HOME=<home> rlm_repl stop.

The offline test is a_recursive_lm_works_on_its_context_in_a_repl_and_batches_sub_llm_calls in tests/runtime.rs.

Source: views/demo/rlm/README.md, about 2 minutes to read.