Experience-Driven Agent Evolution

• Experience-Driven Agent Evolution is a paradigm where agents actively gather, structure, and reuse experiences to continually refine their behavior and problem-solving strategies.
• It employs robust memory architectures such as replay buffers and hierarchical stratification to integrate feedback and update policies dynamically.
• The approach enhances sample efficiency and transferability, as demonstrated by improved benchmark performances in tasks like sim-to-real transfer and long-horizon productivity.

Experience-Driven Agent Evolution refers to a class of agent architectures and learning frameworks in which an agent's behavior, problem-solving strategies, and internal knowledge continually evolve through the active accumulation, structuring, and reuse of its own experience. This paradigm rejects static agent models in favor of closed-loop mechanisms—centering memory, reflection, and self-improvement—that allow agents to adapt, generalize, and become increasingly performant during deployment. Contemporary research operationalizes this principle in LLM agents across domains ranging from web navigation and education simulation to productivity automation and open-ended scientific reasoning.

1. Formal Foundations and Core Principles

Experience-driven evolution is formally grounded in closed-loop frameworks that couple agent-environment interaction with dynamic memory systems and policy updates. The canonical mathematical structure is the Markov Decision Process (MDP), $(\mathcal S, \mathcal A, T, R, \gamma)$, where $\mathcal S$ denotes state space (often textual or multimodal), $\mathcal A$ the finite action set, $T$ the transition dynamics, $R$ the reward function, and $\gamma$ the discount factor (Chen et al., 5 Nov 2025). Some frameworks extend this to the Partially Observable MDP (POMDP) or explicitly goal-conditioned settings (Cai et al., 26 Aug 2025).

Key principles include:

• Experience Acquisition: Agents actively explore, generating diverse trajectories beyond expert demonstrations. Data is gathered from both successes and failures and may be abstracted at several conceptual tiers (e.g., low-level actions, mid-level SOPs, high-level strategies) (Yang et al., 9 Oct 2025, Cai et al., 9 Nov 2025, Cao et al., 11 Dec 2025).
• Memory Organization: Persistent storage structures such as replay buffers (Chen et al., 5 Nov 2025), dual architecture (short- and long-term memory) (Jin et al., 13 Oct 2025), hierarchical procedural/strategic/tool memories (Yang et al., 9 Oct 2025), or structured experience graphs (Tang et al., 17 Oct 2025) are employed.
• Reflection and Task Adaptation: Reflection operators update memories in light of new results, contextualize experience for the current task, and enable curriculum learning via task/value selection (Chen et al., 5 Nov 2025, Cao et al., 11 Dec 2025).
• Policy Evolution: Policies are optimized continuously using experience—via supervised fine-tuning on stored traces, explicit reinforcement learning (PPO/GRPO), or gradient-free mechanisms. Experience integration often couples task-contextual retrieval with in-context prompt augmentation (Cai et al., 9 Nov 2025, Tang et al., 8 Jul 2025).

The functional objective is to maximize expected cumulative returns with respect to an evolving knowledge base, i.e., $$\max_{\pi, \Phi_{\rm learn}} \mathbb{E}_{\xi \sim \pi(\cdot|\mathcal K)} [\sum_t R(s_t, a_t, g)]$$ (Cai et al., 26 Aug 2025).

2. Memory, Distillation, and Experience Synthesis

Memory architectures are central to experience-driven evolution.

• Replay Buffers: Buffer $\mathcal B$ stores transitions (state, action, next state, reward), initialized from offline data and continually updated with fresh rollouts. DreamGym enforces a synthetic/real mix ($\lambda$) to stabilize policy learning (Chen et al., 5 Nov 2025).
• Procedural Memories: Dynamic, non-parametric collections of step-by-step solutions (SOPs) or key decision points, managed by distillation (summarizing, contrasting, failure analysis), scenario-adaptive querying, prompt rewriting, and utility-based pruning (e.g., ReMe’s use/deletion thresholds $\alpha$, $\beta$) (Cao et al., 11 Dec 2025).
• Hierarchical Stratification: Experiences are organized from high-level abstract strategies (“when you saw X, do Y”) down to low-level tool invocations (“call API Z with $args$”) in frameworks such as MUSE (Yang et al., 9 Oct 2025) and FLEX (Cai et al., 9 Nov 2025). Tagging by abstraction and success/failure ‘zone’ enables scalable, inheritance-ready libraries.
• Experience Synthesis: Reasoning-based world models generate next-state/reward samples ($\mathcal M_{\mathrm{exp}}$) by explicit chain-of-thought prompting, incorporating CoT traces in supervised fine-tuning objectives (Chen et al., 5 Nov 2025). This enables fully synthetic data generation for RL task bootstrapping.
• Contextual Adaptation: Scenario-indexed memories are context-matched (cosine similarity of usage vector embeddings), reranked, potentially rewritten for current task applicability, and only then used for prompt augmentation (Cao et al., 11 Dec 2025).

3. Closed-Loop Evolution: Reflection and Policy Update

Experience-driven evolution forms a cyclical process:

1. Interaction and Rollout: The agent collects new data by executing its current policy, possibly informed by retrieved experience pointers.
2. Distillation and Memory Update: Reflection operators summarize, abstract, and integrate successes and failures at various conceptual levels. Selective addition and utility-based deletion mechanisms ensure that the memory doesn’t degrade in quality (Cao et al., 11 Dec 2025, Qian et al., 7 May 2024).
3. Curriculum and Task Adaptation: Novelty-aware or entropy-driven curriculum components generate new tasks or task variations that are neither too easy nor too hard, maximizing reward-variance for maximal policy learning (Chen et al., 5 Nov 2025).
4. Policy Optimization: Mixed batches from memory (real and synthetic) guide RL updates (PPO, GRPO) or serve as in-context data for policy distillation. Some architectures employ gradient-free update rules (FLEX) (Cai et al., 9 Nov 2025).
5. Generalization and Transfer: Memories support zero-shot transfer to new domains or tasks, as observed in sim-to-real and cross-environment performance gains (Chen et al., 5 Nov 2025, Cai et al., 9 Nov 2025, Yang et al., 9 Oct 2025).

This continuous evolution is often realized in Plan–Execute–Reflect–Memorize cycles (Yang et al., 9 Oct 2025). Monotonic improvements in performance measures $R(k)$ across iterations are empirically observed.

4. Empirical Results and Comparative Performance

Across a wide array of benchmarks and domains, experience-driven mechanisms yield substantial efficacy:

• RL and Synthetic Environments: DreamGym achieves 63.9% (WebShop), 66.3% (ALFWorld), and 9.1–10.9% (WebArena) success rates with zero real-world data, surpassing prior baselines by over 30 pp on non-RL-ready tasks. Sim-to-real warm-up requires 90% fewer real-world samples for SOTA performance (Chen et al., 5 Nov 2025).
• Long-Horizon Productivity: MUSE sets new SOTA on the 175-task TAC benchmark with 51.7% average partial score, a 20% gain over SFT and memoryless agents. Zero-shot generalization is bolstered by 10% absolute margin (Yang et al., 9 Oct 2025).
• Computation Efficiency: ReMe shows that Qwen3-8B with dynamic memory exceeds the larger memoryless Qwen3-14B (Pass@4, 55.03% vs 54.65%) and approaches the 32B model (Cao et al., 11 Dec 2025). FLEX demonstrates power-law scaling in accuracy with library size. Memory and batch sizes remain modest relative to full model finetuning cost (Cai et al., 9 Nov 2025).
• Ablations: Removing replay, explicit reasoning, or curriculum components in DreamGym causes 4–8 pp performance drops, underscoring component necessity (Chen et al., 5 Nov 2025). Memory granularity (keypoint-level) and scenario-adaptive retrieval in ReMe are both critical (Cao et al., 11 Dec 2025).

5. Comparison with Prior and Alternative Approaches

Traditional agent paradigms—imitation learning, static workflow composition, one-shot meta-learning—are contrasted with experience-driven evolution along several axes:

• Adaptivity vs. Stasis: Static fine-tuning or script-based agents cannot improve at deployment, are sample-inefficient, and generalize poorly outside observed data (Yang et al., 9 Oct 2025, Cai et al., 26 Aug 2025).
• Passive vs. Dynamic Memory: Append-only demonstration banks are prone to drift, dilution, and inability to regularize to context shift (Cao et al., 11 Dec 2025, Qian et al., 7 May 2024). Dynamic systems maintain relevance by pruning and utility tracking.
• Task Diversity: Hand-coded curriculum or offline RL surrogates lack the entropy and challenge modulation provided by agent-in-the-loop curriculum learning (Chen et al., 5 Nov 2025, Zhai et al., 13 Nov 2025).
• Sample Complexity and Computation: Experience-driven replay, synthetic rollout, and context modulation markedly reduce the environmental interaction and API call costs while maintaining or improving sample efficiency (Chen et al., 5 Nov 2025, Feng et al., 23 May 2025).

6. Open Challenges and Future Directions

Ongoing research targets several unresolved areas:

• Long-Horizon Credit Assignment: Effective propagation of feedback across hundreds of steps without dense external rewards remains an outstanding problem for truly open-ended domains (Zhang et al., 9 Oct 2025).
• Memory Scaling and Indexing: As experience libraries grow, vector retrieval bottlenecks, staleness, and semantic drift present new challenges for both efficiency and relevance (Cao et al., 11 Dec 2025, Cai et al., 9 Nov 2025).
• Sim-to-Real and Cross-Domain Transfer: Formalizing and automating transfer mechanisms—enabling accumulated experience to bootstrap learning in new domains—remains an active area (Yang et al., 9 Oct 2025, Chen et al., 5 Nov 2025).
• Safety, Correctness, and Negative Experience: Richer frameworks for negative experience, counterfactual memory, and strategic forgetting are needed to avoid brittle overfitting or hallucinated error propagation (Qian et al., 7 May 2024).
• Human-Machine Collaboration: Integrating human feedback, demonstration, and audit into closed-loop experience growth offers avenues for aligned, trustworthy agents (Jin et al., 13 Oct 2025, Zhang et al., 9 Oct 2025).

Emerging research supports the notion that experience-driven agent evolution—anchored in structured memory, adaptive reflection, and automated task generation—is a fundamental pathway for scalable, autonomous, and continually improving agentic intelligence (Chen et al., 5 Nov 2025, Cao et al., 11 Dec 2025, Yang et al., 9 Oct 2025, Cai et al., 9 Nov 2025).