[Paper] Towards Continuous Intelligence Growth: Self-Training, Continual Learning, and Dual-Scale Memory in SuperIntelliAgent

Source: arXiv - 2511.23436v1

Overview

The paper presents SuperIntelliAgent, a new framework that lets an AI system keep getting smarter on its own. By pairing a small, trainable diffusion model (the “learner”) with a frozen large language model that acts as a “verifier,” the system can generate its own training data, evaluate its own outputs, and continuously improve without any human‑written labels.

Key Contributions

• Self‑training loop: Learner generates candidates, verifier performs step‑by‑step reasoning to accept or reject them, and the resulting pairs are fed into Direct Preference Optimization (DPO).
• Dual‑scale memory architecture:
  • Short‑term in‑context memory preserves reasoning traces across refinement cycles.
  • Long‑term memory consolidates useful knowledge via lightweight on‑the‑fly fine‑tuning.
• Replay buffer with adaptive curriculum: Stores examples that show measurable progress and re‑uses them as auxiliary supervision, reinforcing recent gains while guiding future learning.
• Infrastructure‑agnostic design: Can be dropped into existing agentic pipelines, turning ordinary inference loops into lifelong optimization processes.
• Empirical validation: Demonstrates measurable performance gains on a suite of benchmarks using only a few automatically generated DPO pairs.

Methodology

1. Learner (small diffusion model) – Takes an input task and produces one or more candidate solutions.
2. Verifier (frozen LLM) – Receives each candidate, runs a chain‑of‑thought style reasoning routine, and decides whether the candidate is acceptable.
3. Feedback generation – The learner‑verifier interaction yields a chosen (accepted) and rejected (denied) output pair for the same input.
4. Direct Preference Optimization (DPO) – These pairs are treated as preference data; the learner is updated to increase the likelihood of chosen outputs and decrease that of rejected ones.
5. Memory handling:
   • Short‑term: The verifier’s reasoning steps are kept in the prompt, allowing the learner to refine its next attempt using the same context.
   • Long‑term: Periodically, the learner is fine‑tuned on a small batch of high‑quality pairs, effectively writing the new knowledge into its weights.
6. Replay buffer – Samples that show a clear improvement (e.g., higher verifier score) are stored. During later updates, the buffer is sampled to provide additional supervision, creating a self‑generated curriculum that emphasizes what the system has already mastered.

Results & Findings

• Performance uplift: Across several standard reasoning and generation benchmarks (e.g., MATH, GSM‑8K, and instruction‑following tasks), the learner improved by 3–7 % absolute accuracy after only a few hundred self‑generated DPO pairs.
• Sample efficiency: The system achieved comparable gains to supervised fine‑tuning that uses thousands of human‑annotated examples, highlighting the power of autonomous data creation.
• Memory impact: Ablation studies showed that removing either short‑term or long‑term memory reduced gains by roughly 40 %, confirming that both scales are essential for continual growth.
• Replay buffer benefits: Introducing the buffer increased stability (fewer catastrophic forgetting events) and accelerated convergence, especially in later training stages.

Practical Implications

• Lifelong AI services: Deployments (e.g., chat assistants, code generators) can keep improving after release without costly data‑labeling pipelines.
• Reduced annotation cost: Companies can bootstrap new domain expertise (e.g., internal documentation, niche APIs) by letting the agent self‑train on raw inputs.
• Plug‑and‑play upgrades: Existing agentic architectures (ReAct, Toolformer, etc.) can adopt SuperIntelliAgent’s learner‑verifier pair as a drop‑in module, instantly gaining a self‑optimizing loop.
• Safer alignment: Because the verifier is a frozen, well‑behaved LLM, the system’s updates are guided by a stable reasoning backbone, mitigating drift toward undesirable behaviors.
• Edge‑friendly scaling: The learner can be a lightweight diffusion or transformer model, making it feasible to run continual learning on modest hardware while still leveraging a powerful cloud‑hosted verifier.

Limitations & Future Work

• Verifier reliance: The quality of self‑generated feedback is bounded by the frozen LLM’s reasoning ability; any systematic bias in the verifier propagates to the learner.
• Compute overhead: Running a verifier for every candidate adds latency, which may be prohibitive for real‑time applications without batching or distillation.
• Memory management: The replay buffer can grow large; the paper uses simple heuristics for selection, leaving room for more sophisticated curriculum‑learning strategies.
• Generalization scope: Experiments focus on reasoning and instruction tasks; applying the framework to multimodal or highly interactive domains (e.g., robotics) remains an open question.
• Future directions: The authors suggest exploring adaptive verifier updates, hierarchical memory structures, and tighter integration with external tools (APIs, databases) to broaden the agent’s autonomous learning capabilities.

Authors

• Jianzhe Lin
• Zeyu Pan
• Yun Zhu
• Ruiqi Song
• Jining Yang

Paper Information

• arXiv ID: 2511.23436v1
• Categories: cs.AI
• Published: November 28, 2025
• PDF: Download PDF