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arxiv: 2605.03408 · v1 · submitted 2026-05-05 · 💻 cs.LG · cs.AI

Discovering Reinforcement Learning Interfaces with Large Language Models

Akshat Singh Jaswal , Ashish Baghel , Paras Chopra This is my paper

Pith reviewed 2026-05-07 17:17 UTC · model grok-4.3

classification 💻 cs.LG cs.AI
keywords reinforcement learninglarge language modelsevolutionary searchreward designobservation mappingtask interface discoverypolicy training feedback
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The pith

An LLM-guided evolutionary process jointly designs observation mappings and reward functions to build complete RL interfaces from raw simulator states using only trajectory success signals.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper shows that large language models can generate executable code for both how to turn raw simulator states into observations and how to compute rewards, then iteratively improve these candidates through actual policy training runs. Success is measured only by whether a trajectory solves the task, with no hand-crafted dense rewards or fixed observation spaces. Jointly evolving both components succeeds on discrete gridworlds and continuous locomotion and manipulation tasks, but optimizing just the observations or just the rewards causes failure on at least one domain. This matters because it points toward automating the interface engineering step that currently limits applying RL to new problems.

Core claim

LIMEN produces candidate interfaces as executable programs and refines them via LLM-guided evolutionary search driven by policy training feedback; across the tested tasks, joint evolution of observations and rewards discovers effective interfaces from raw state given only a trajectory-level success metric, while optimizing either component alone fails on at least one domain.

What carries the argument

The LLM-guided evolutionary framework that generates and iteratively refines executable code for observation mappings and reward functions based on downstream policy training performance.

If this is right

  • RL systems could be applied to new tasks with substantially less manual design of observations and rewards.
  • Observation and reward components often require co-design; single-component optimization is insufficient in some environments.
  • Automatic interface discovery can work from raw simulator state using only binary trajectory success as supervision.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The method might be extended to simulators where human reward design is especially difficult or where observation spaces are high-dimensional.
  • Discovered interfaces could be tested for transfer to related tasks not seen during evolution.

Load-bearing premise

That LLM-generated executable code combined with evolutionary search and policy training feedback can reliably produce functional interfaces without excessive compute or frequent failure to escape poor local optima across diverse domains.

What would settle it

A new continuous or discrete control task on which joint evolution fails to produce any policy that solves the task while a human-designed interface succeeds, or on which optimizing only observations or only rewards matches or exceeds the joint result.

Figures

Figures reproduced from arXiv: 2605.03408 by Akshat Singh Jaswal, Ashish Baghel, Paras Chopra.

Figure 1
Figure 1. Figure 1: Overview of the LIMEN framework. The outer loop performs evolutionary search: LIMEN view at source ↗
Figure 2
Figure 2. Figure 2: Evaluation environments. Top: XLand-MiniGrid tasks of increasing compositional complexity— (a) object pickup among distractors, (b) relational placement, (c) multi-step rule chain across rooms. Bottom: MuJoCo tasks— (d) quadruped push recovery, (e) manipulator trajectory tracking. I = (ϕ, R), where: • ϕ : S → O is an observation mapping, • R : S × A × S → R is a reward function. The interface transforms th… view at source ↗
Figure 3
Figure 3. Figure 3: Evolution progress of LIMEN showing candidate interfaces, crash events, and improvements view at source ↗
Figure 4
Figure 4. Figure 4: Learning curves for LIMEN and ablations across five tasks. Success rate versus environment view at source ↗
Figure 5
Figure 5. Figure 5: Independent LLM samples (no evolution) versus the best interface found by LIMEN across view at source ↗
Figure 6
Figure 6. Figure 6: Robustness under distribution shift for the Go1 push recovery and Panda tracking tasks. To evaluate robustness, we retrain the best robotics in￾terfaces under perturbed dynamics (Go1: 25M steps, Panda: 15M steps, 5 seeds each). Performance de￾grades continuously under both perturbation types rather than collapsing to zero. For Go1, doubling push force reduces success from 50.3% to 17.8%, while in￾creasing … view at source ↗
Figure 7
Figure 7. Figure 7: Evolution progress across five independent seeds per XLand-MiniGrid task. Each line shows view at source ↗
read the original abstract

Reinforcement learning systems rely on environment interfaces that specify observations and reward functions, yet constructing these interfaces for new tasks often requires substantial manual effort. While recent work has automated reward design using large language models (LLMs), these approaches assume fixed observations and do not address the broader challenge of synthesizing complete task interfaces. We study RL task interface discovery from raw simulator state, where both observation mappings and reward functions must be generated. We propose LIMEN (Code available at https://github.com/Lossfunk/LIMEN), a LLM guided evolutionary framework that produces candidate interfaces as executable programs and iteratively refines them using policy training feedback. Across novel discrete gridworld tasks and continuous control domains spanning locomotion and manipulation, joint evolution of observations and rewards discovers effective interfaces given only a trajectory-level success metric, while optimizing either component alone fails on at least one domain. These results demonstrate that automatic construction of RL interfaces from raw state can substantially reduce manual engineering and that observation and reward components often benefit from co-design, as single-component optimization fails catastrophically on at least one domain in our evaluation suite.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The manuscript introduces LIMEN, an LLM-guided evolutionary framework that generates executable programs for both observation mappings and reward functions from raw simulator state in RL environments. It claims that joint evolution of these components, guided solely by trajectory-level success feedback from policy training, produces effective interfaces across novel discrete gridworld tasks and continuous control domains (locomotion and manipulation), whereas optimizing either observations or rewards in isolation fails catastrophically on at least one domain in the evaluation suite.

Significance. If the empirical results hold under rigorous evaluation, the work would demonstrate a practical path toward automating RL interface construction, substantially reducing manual engineering effort. The open availability of code at the provided GitHub repository is a clear strength for reproducibility. The finding that observation and reward components benefit from co-design (rather than independent optimization) would be a useful insight for the RL community if supported by robust evidence.

major comments (3)
  1. [Abstract and Experiments] Abstract and Experiments section: The central claim of success for joint evolution and catastrophic failure for single-component baselines is presented without any quantitative metrics (e.g., success rates, returns, or learning curves), number of independent runs, random seeds, or statistical tests. This directly weakens the ability to evaluate whether the reported superiority is reliable or reproducible.
  2. [Method] Method section (evolutionary framework description): The scoring of candidate interfaces relies on policy training feedback, yet no variance-reduction techniques are described, such as averaging over multiple random seeds per candidate, surrogate models for interface quality, or early stopping based on learning curves. Given that RL training (especially in continuous domains) is sensitive to seeds and hyperparameters, this omission risks unreliable ranking of interfaces and undermines the joint-vs-single comparison.
  3. [Experiments] Experiments section: The evaluation does not specify how 'catastrophic failure' of single-component baselines is measured or quantified across domains, nor does it report implementation details such as the number of generations, population size, or LLM prompting strategy. These details are load-bearing for assessing whether the joint evolution advantage is robust rather than an artifact of particular experimental choices.
minor comments (2)
  1. [Abstract] The abstract provides a GitHub link for code, which aids reproducibility; ensure the paper includes sufficient pseudocode or algorithmic details to allow independent reimplementation without relying solely on the repository.
  2. [Introduction] Clarify the precise definition of the 'trajectory-level success metric' used for feedback, including how it is computed from raw simulator trajectories in both discrete and continuous settings.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive comments, which highlight important aspects of reproducibility and experimental rigor. We have revised the manuscript to incorporate quantitative metrics, clarify variance-reduction practices, and provide missing implementation details. These changes strengthen the presentation without altering the core claims. We respond to each major comment below.

read point-by-point responses

  1. Referee: [Abstract and Experiments] Abstract and Experiments section: The central claim of success for joint evolution and catastrophic failure for single-component baselines is presented without any quantitative metrics (e.g., success rates, returns, or learning curves), number of independent runs, random seeds, or statistical tests. This directly weakens the ability to evaluate whether the reported superiority is reliable or reproducible.

    Authors: We agree that quantitative support is essential for assessing reliability. The original experiments section contained some aggregate results, but these were not highlighted in the abstract or accompanied by run counts and tests. In the revised version, we have added specific metrics to the abstract (e.g., mean returns and success rates) and expanded the experiments section with results from 5 independent runs per condition using distinct random seeds, including standard deviations and p-values from paired t-tests comparing joint versus single-component evolution. Learning curves for representative tasks are now included in the appendix. revision: yes

  2. Referee: [Method] Method section (evolutionary framework description): The scoring of candidate interfaces relies on policy training feedback, yet no variance-reduction techniques are described, such as averaging over multiple random seeds per candidate, surrogate models for interface quality, or early stopping based on learning curves. Given that RL training (especially in continuous domains) is sensitive to seeds and hyperparameters, this omission risks unreliable ranking of interfaces and undermines the joint-vs-single comparison.

    Authors: We acknowledge the concern regarding training stochasticity. The revised method section now details our variance-reduction procedure: each candidate interface is evaluated by training the downstream policy for 3 independent random seeds, with the final score computed as the average trajectory-level success metric. We also apply early stopping when the moving average of episode returns plateaus for 10 consecutive episodes. Surrogate models were not employed due to the computational cost of LLM-based program generation, but we note this limitation and its potential for future mitigation. revision: yes

  3. Referee: [Experiments] Experiments section: The evaluation does not specify how 'catastrophic failure' of single-component baselines is measured or quantified across domains, nor does it report implementation details such as the number of generations, population size, or LLM prompting strategy. These details are load-bearing for assessing whether the joint evolution advantage is robust rather than an artifact of particular experimental choices.

    Authors: We agree these specifics are necessary for reproducibility. The revised experiments section now defines 'catastrophic failure' explicitly as a final policy return below 10% of the task-specific maximum or task completion in fewer than 10% of evaluation episodes. We report the evolutionary hyperparameters (10 generations, population size of 20) and include the full LLM prompting templates and generation strategy in a new appendix subsection, along with pseudocode for the joint evolution loop. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical method grounded in external policy feedback

full rationale

The paper describes an LLM-guided evolutionary search that generates executable observation and reward code, then refines candidates using feedback from actual policy training runs on the resulting interfaces. This evaluation loop relies on external simulator interactions and RL optimizer performance rather than any self-referential definition, fitted parameter renamed as a prediction, or load-bearing self-citation. No equations, uniqueness theorems, or ansatzes are invoked to derive the central claim; superiority of joint evolution is shown through direct empirical comparison to single-component baselines across discrete and continuous domains. The derivation chain is therefore self-contained and non-tautological.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the ability of standard RL training to provide reliable fitness signals for interface candidates and on the LLM's capacity to produce syntactically valid, semantically relevant code; no new physical entities or ad-hoc constants are introduced.

axioms (1)
  • domain assumption Policy training on a candidate interface yields a usable scalar success metric that can guide evolutionary selection
    The method evaluates interfaces by training policies and using trajectory-level success; this is invoked throughout the abstract description of the evolutionary loop.

pith-pipeline@v0.9.0 · 5486 in / 1193 out tokens · 119252 ms · 2026-05-07T17:17:47.392373+00:00 · methodology

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Reference graph

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