Dual Pattern Equilibrium Framework (W+X=1): A Robust Reward Optimization Architecture for Autonomous Local Coding Agents - Hardened V2

Abstract

chabira abderraouf Autonomous developer agents operating on commodity hardware face a critical trade-off between code correctness and resource stability. Traditional reinforcement learning from human feedback (RLHF) and PPO-based code optimization rely on binary success/failure rewards, which are vulnerable to reward hacking through overconfident token generation and uncontrolled VRAM consumption. In this work, we present the Dual Pattern Equilibrium Framework V2 (W+X=1), a hardened reward shaping mechanism. We reinterpret the conservation law as a dynamic equilibrium target: W_ideal = 1 - X, where X is non-fakeable environmental friction derived from real-time VRAM telemetry (torch.cuda.memory_allocated). To prevent overconfidence hacking, internal willpower (W_actual) is computed via calibrated Shannon entropy (1 - H_norm) rather than raw softmax mean. The composite reward R_total = (alpha_W_actual_Pc) - (lambda_X_(1-Pe)) - (gamma_|W_actual-W_ideal|) + (delta_G) - (omega*I_syntax) integrates correctness, friction, conservation deviation, normalized cognitive growth G=tanh(E/(1+D)), and syntax validity. All components are min-max normalized to prevent gradient dominance. Evaluated conceptually for SWE-Bench Lite and 27B-class open models (e.g., Gemma 2/3), the framework eliminates two major exploit vectors and guides agents toward resource-optimal equilibrium. This preprint provides the full hardened Python implementation for Kaggle TRL/PPO integration.