Conditioning Prompts for Naturalistic Yet Verifiable Terminal Tasks

Problem & Motivation

The Endless Terminals pipeline generates terminal-use tasks for RL agents, but the resulting tasks resemble competitive programming problems rather than naturalistic user requests.

500

Simulated Tasks

Conditioning Strategies

Task Categories

The Naturalness-Verifiability Tension

Agents trained on formal, fully-specified task descriptions may fail to generalize to underspecified, context-dependent requests encountered in deployment. This work formulates the challenge as a multi-objective optimization problem: maximize naturalistic language while preserving automated verification capability. Six conditioning strategies are evaluated across the full design space, revealing a smooth Pareto frontier and actionable architectural guidelines.

Methods

Six conditioning strategies parameterized by persona strength, specification retention, exemplar count, and decoupling degree.

Baseline (Current Pipeline)

No naturalistic conditioning. Direct specification generation.

p=0.00, r=1.00, e=0, d=0.00

Persona-Conditioned Rewriting

Two-pass pipeline with sampled user persona rewriting.

p=0.85, r=0.40, e=3, d=0.90

Dual-Objective Single Pass

Single pass with explicit dual naturalness-verifiability objectives.

p=0.50, r=0.70, e=5, d=0.30

Adversarial Naturalness Filter

Generate-then-filter with a naturalness discriminator.

p=0.70, r=0.55, e=2, d=0.60

Minimal Rewrite

Conservative approach with light persona conditioning.

p=0.30, r=0.85, e=1, d=0.20

Full Decoupling

Maximum separation between verification substrate and surface form.

p=0.90, r=0.30, e=4, d=1.00

Strategy Comparison

Aggregate results across 500 simulated tasks showing the trade-offs between naturalness, verifiability, and their harmonic mean.

Naturalness vs Verifiability

Harmonic Mean Ranking

Full Metrics Table

Strategy	Naturalness	Verifiability	Harmonic Mean	Resolvability	Diversity
Baseline	0.0358	0.6669	0.0651	0.3395	0.4280
Persona Rewrite	0.7584	0.4531	0.5654	0.6028	0.8756
Dual Objective	0.5076	0.4851	0.4945	0.4205	0.7158
Adversarial Filter	0.5655	0.5350	0.5483	0.5314	0.7682
Minimal Rewrite	0.2272	0.5903	0.3243	0.3913	0.5769
Full Decouple	0.8631	0.3886	0.5334	0.6172	0.9900

Pareto Frontier Analysis

Sweeping 50 parameter configurations reveals a smooth trade-off curve with 38 Pareto-optimal points.

Naturalness-Verifiability Frontier

Harmonic Mean vs Persona Strength

38/50

Pareto-Optimal Configs

0.036 - 0.884

Naturalness Range

0.357 - 0.667

Verifiability Range

Complexity Scaling

Task complexity systematically degrades both naturalness and verifiability across all strategies.

Naturalness vs Complexity

Verifiability vs Complexity

Complexity Scaling Table

Strategy	Simple (Nat/Ver)	Moderate (Nat/Ver)	Complex (Nat/Ver)	Expert (Nat/Ver)
Baseline	0.059 / 0.741	0.042 / 0.691	0.024 / 0.641	0.020 / 0.594
Persona Rewrite	0.803 / 0.526	0.775 / 0.482	0.743 / 0.430	0.710 / 0.372
Adversarial Filter	0.608 / 0.613	0.576 / 0.559	0.545 / 0.511	0.522 / 0.461
Dual Objective	0.556 / 0.562	0.528 / 0.510	0.489 / 0.464	0.466 / 0.410
Minimal Rewrite	0.270 / 0.668	0.235 / 0.614	0.212 / 0.563	0.188 / 0.520
Full Decouple	0.907 / 0.464	0.883 / 0.410	0.848 / 0.368	0.825 / 0.316

Information-Theoretic Analysis

Decoupling the verification substrate from the surface form enables environment-based recovery of omitted specification details.

Information Loss Budget

Mutual Information Proxy

Information-Theoretic Metrics

Strategy	Info Loss	Env Recovery	Net Gap	MI Proxy	Correlation
Baseline	0.0000	0.3000	0.0000	0.1390	0.4926
Persona Rewrite	0.6000	0.6600	0.0000	0.2419	0.6193
Dual Objective	0.3000	0.3900	0.0000	0.2257	0.6027
Adversarial Filter	0.4500	0.5250	0.0000	0.2148	0.5910
Minimal Rewrite	0.1500	0.3750	0.0000	0.2220	0.5988
Full Decouple	0.7000	0.6900	0.0100	0.1942	0.5673

Key Findings

Summary of the main results and their implications for prompt conditioning in RL task generation pipelines.

Adversarial filtering achieves the best balance. With a harmonic mean of 0.5483, the adversarial naturalness filter strategy provides the optimal trade-off between naturalness (0.5655) and verifiability (0.5350), outperforming all other strategies on this combined metric.

Smooth Pareto frontier with 38 non-dominated points. Sweeping 50 parameter configurations reveals a continuous trade-off surface from high verifiability/low naturalness to high naturalness/low verifiability, with no abrupt transitions or gaps.

Decoupling enables near-zero information gaps. Strategies with high decoupling degree achieve near-zero net information gaps despite significant specification omission, because the environment provides sufficient context for task resolution. Persona rewriting achieves exactly zero net gap with 0.600 information loss.

Complexity is the primary degradation factor. Expert-level tasks show verifiability drops of 0.153 for persona rewriting and 0.146 for adversarial filtering compared to simple tasks. All strategies degrade with increasing complexity, but the adversarial filter maintains the most stable balance.

Category-specific performance varies significantly. File operations achieve the highest naturalness (0.819 under persona rewriting) while network configuration shows the lowest (0.689). This suggests category-adaptive conditioning may further improve results.