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CosmoCore-Evo: Evolutionary Dream-Replay Reinforcement Learning for Adaptive Code Generation

Santhosh Kumar Ravindran

TL;DR

CosmoCore-Evo addresses the brittleness of LLM-based code generation under distribution shifts by coupling affective dream-replay with evolutionary algorithms. The method mutates high‑fitness replay trajectories during offline nocturnal phases, enabling exploration beyond trained patterns while preserving enterprise-relevant objectives through multi‑objective fitness terms. Empirical results show substantial gains in novelty and faster adaptation on shifted HumanEval and BigCodeBench tasks, with ablations confirming the essential role of mutation and enterprise fitness signals. The work highlights a practical path for deploying more adaptive, novel, and regulation-aware code agents in dynamic enterprise settings, and lays groundwork for broader anthropological extensions in future work.

Abstract

Building on the affective dream-replay reinforcement learning framework of CosmoCore, we introduce CosmoCore-Evo, an extension that incorporates evolutionary algorithms to enhance adaptability and novelty in code generation tasks. Inspired by anthropological aspects of human evolution, such as natural selection and adaptation in early hominids, CosmoCore-Evo treats RL trajectories as ``genomes'' that undergo mutation and selection during the nocturnal replay phase. This mechanism allows agents to break free from trained patterns, fostering emergent behaviors and improved performance in distribution-shifted environments, such as changing APIs or novel libraries. We augment the Dream Queue with evolutionary operations, including mutation of high-fitness trajectories and enterprise-tuned fitness functions that incorporate efficiency, compliance, and scalability metrics. Evaluated on extended benchmarks including HumanEval variants with shifts, BigCodeBench, and a custom PySpark pipeline simulation, CosmoCore-Evo achieves up to 35% higher novelty in solutions and 25% faster adaptation compared to the original CosmoCore and baselines like PPO and REAMER. Ablations confirm the role of evolutionary components in bridging the sentient gap for LLM agents. Code for replication, including a toy simulation, is provided.

CosmoCore-Evo: Evolutionary Dream-Replay Reinforcement Learning for Adaptive Code Generation

TL;DR

CosmoCore-Evo addresses the brittleness of LLM-based code generation under distribution shifts by coupling affective dream-replay with evolutionary algorithms. The method mutates high‑fitness replay trajectories during offline nocturnal phases, enabling exploration beyond trained patterns while preserving enterprise-relevant objectives through multi‑objective fitness terms. Empirical results show substantial gains in novelty and faster adaptation on shifted HumanEval and BigCodeBench tasks, with ablations confirming the essential role of mutation and enterprise fitness signals. The work highlights a practical path for deploying more adaptive, novel, and regulation-aware code agents in dynamic enterprise settings, and lays groundwork for broader anthropological extensions in future work.

Abstract

Building on the affective dream-replay reinforcement learning framework of CosmoCore, we introduce CosmoCore-Evo, an extension that incorporates evolutionary algorithms to enhance adaptability and novelty in code generation tasks. Inspired by anthropological aspects of human evolution, such as natural selection and adaptation in early hominids, CosmoCore-Evo treats RL trajectories as ``genomes'' that undergo mutation and selection during the nocturnal replay phase. This mechanism allows agents to break free from trained patterns, fostering emergent behaviors and improved performance in distribution-shifted environments, such as changing APIs or novel libraries. We augment the Dream Queue with evolutionary operations, including mutation of high-fitness trajectories and enterprise-tuned fitness functions that incorporate efficiency, compliance, and scalability metrics. Evaluated on extended benchmarks including HumanEval variants with shifts, BigCodeBench, and a custom PySpark pipeline simulation, CosmoCore-Evo achieves up to 35% higher novelty in solutions and 25% faster adaptation compared to the original CosmoCore and baselines like PPO and REAMER. Ablations confirm the role of evolutionary components in bridging the sentient gap for LLM agents. Code for replication, including a toy simulation, is provided.
Paper Structure (16 sections, 2 equations, 2 figures, 2 tables, 1 algorithm)

This paper contains 16 sections, 2 equations, 2 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Affective and Emotion-Inspired RL
  4. Prioritized Experience Replay and Buffer Management
  5. Evolutionary Algorithms in RL
  6. RL for Code Generation and LLM Limitations
  7. Proposed Method: CosmoCore-Evo
  8. Recap of CosmoCore
  9. Experiments
  10. Experimental Setup
  11. Toy Code Synthesis Environment
  12. Main Code Generation Benchmarks
  13. Ablations
  14. Discussion
  15. Conclusion
  16. ...and 1 more sections

Figures (2)

  • Figure 1: Architecture of CosmoCore-Evo. Light blue nodes represent original CosmoCore components. Red-toned nodes and arrows highlight the evolutionary extensions that enrich the Dream Queue with mutated high-fitness trajectories.
  • Figure 2: Learning curves on the toy code synthesis task (mean $\pm$ std over 20 seeds). CosmoCore-Evo discovers near-optimal solutions significantly faster due to evolutionary mutation introducing high-reward variants into the replay buffer.