Where is the natural boundary of RLM as a reasoning primitive?

[matomoniwano]

I’ve been reading the RLM paper and thinking about the scope of recursion as a reasoning primitive.

One question I keep coming back to is where the authors see the natural boundary of RLM:

Should RLM be treated strictly as a pure reasoning operator — operating over a provided state and returning a trace/output — with all control, memory, and orchestration handled externally?

Or do you imagine a future direction where some aspects of control flow (e.g. termination signals, intermediate state shaping, or recursive depth adaptation) become part of the RLM abstraction itself?

I’m especially curious how you think about preserving the conceptual clarity of recursive reasoning while integrating it into larger systems that necessarily maintain state and long-horizon context.

[apenab]

Hi @matomoniwano.
From my experience building rlm-runtime, I tend to see RLM primarily as a reasoning primitive, not as a full control or agent abstraction.

In practice, I’ve found it much cleaner to keep:

• recursion and reasoning inside the RLM loop
• control flow, memory ownership, and orchestration outside, in the runtime / environment

In rlm-runtime, the model interacts with state through a persistent REPL and deterministic helpers, but it doesn’t “own” long-horizon state or execution. That separation keeps the system understandable and debuggable.

I think some light structure at the interface level (explicit termination signals, bounded recursion, structured intermediate outputs) makes sense, as long as it stays declarative. What I’d avoid is pushing implicit planning or hidden control flow into the RLM itself.

My mental model is basically:
RLM = reasoning engine
runtime = state + control

That boundary keeps the recursive reasoning idea clear, while still letting it plug into larger systems without turning it into a black-box agent.

Here my implementation: https://github.com/apenab/rlm-runtime

Cheers.

[matomoniwano]

@apenab Thanks, this framing really resonates.

I like the clarity of
RLM = reasoning engine
runtime = state + control

Especially the point about debuggability — keeping long-horizon state and orchestration outside the recursive loop makes it much easier to reason about failure modes and inspect traces without collapsing everything into a single black box.

One thing I’ve been thinking about is whether the interface between the two becomes the most important design surface over time. Not pushing control into RLM, but making the boundary more explicit and declarative, as you mentioned.

For example:

structured termination signals instead of implicit “done”

bounded or adaptive recursion as an externally-enforced constraint

intermediate artifacts that are intentionally shaped for downstream consumers (planners, evaluators, runtimes)

That still preserves the conceptual purity of recursion-as-reasoning, while letting larger systems compose around it without smuggling planning logic into the model itself.

Your REPL + deterministic helper approach feels like a strong instantiation of that idea — the model reasons with state, but doesn’t own it. I’m curious how you think about evolving that interface without increasing coupling.

Thanks for sharing rlm-runtime, super helpful reference.

To share a small data point: I recently tried applying this same separation while experimenting with an RLM integration in a larger agent framework.

In that setup, RLM is kept strictly as an optional, non-owning component — essentially a reasoning plugin that sits behind existing planning boundaries. Memory, control flow, and long-horizon orchestration stay entirely outside the RLM loop.

PR for context (if useful):
elizaOS/eliza#6383

The takeaway for me was similar to what you described: treating RLM as a pure reasoning primitive with explicit interfaces makes integration cleaner and avoids conflating recursion with control. Curious if this aligns with what you’ve observed when RLM is embedded into more complex systems.

[apenab]

Hellou @matomoniwano , sorry for the late reply. The idea of rlm plugin sound really nice. I wondered the following: An RLM executes LLM-generated code in a Python REPL. What could go wrong? prompt-injections, llm bugs, sandbox escapes...etc.

I'm introducing Monty, a Rust-based sandboxed REPL as an alternative to the standard Python REPL, offering enhanced security and hardened protections against malicious code execution. apenab/pyrlm-runtime#9