Early Experience Paradigm

• Early Experience Paradigm is defined as the principle that initial rewarding or salient encounters fundamentally shape learning, decision-making, and behavioral biases in both natural and artificial systems.
• The paradigm is operationalized through mechanisms such as eligibility traces, memory distortion, and sensorimotor integration, which quantify its impact on sequential decision-making and neural reorganization.
• It underpins practical applications in reinforcement learning, predictive coding, and lifelong learning by emphasizing strategic management of formative experiences to enhance long-term adaptability.

The Early Experience Paradigm refers to the theoretical and empirical observation that an agent’s behavior, learning dynamics, or decision-making strategies are disproportionately shaped by initial experiences—whether those experiences are rewarding, salient, or simply the first to occur in a developmental or training sequence. Across biological systems, computational models, and artificial agents, research demonstrates that early encounters not only anchor subsequent evaluations and choices but can also establish persistent biases, reinforce extended action sequences, and reconfigure neural or agent architectures in ways that persist well beyond the initial phase.

1. Foundations in Sequential Decision Making and Eligibility Traces

Early work in reinforcement learning formalizes the impact of early experience via mechanisms such as eligibility traces. In classic temporal-difference (TD) learning (e.g., TD-0), only the immediate pre-reward state-action pair receives feedback. Eligibility trace models instead maintain a decaying memory of all prior state-action pairs, so that a single reward can retroactively reinforce entire sequences ("one-shot learning"). These traces are mathematically specified by:

$$e_n(s,a) = \begin{cases} 1 & \text{if } s = s_n \text{ and } a = a_n \ \gamma \lambda e_{n-1}(s,a) & \text{otherwise} \end{cases}$$

with discount factor $\gamma$ and decay parameter $\lambda$ controlling temporal spread (Lehmann et al., 2017).

Upon receiving a reward, Q-values are updated via: $$Q(s,a) \leftarrow Q(s,a) + \alpha \cdot \mathrm{RPE} \cdot e_n(s,a)$$ where the reward prediction error (RPE) is

$$\mathrm{RPE} = r_{n+1} + \gamma \max_a Q(s_{n+1}, a) - Q(s_n, a_n)$$

Empirical studies with human subjects show that after a single rewarded episode, participants demonstrate increased choice bias and physiological markers (e.g., pupil dilation) for not only the immediately preceding action but also for actions two steps removed from reward, evidencing extended reinforcement of early experience sequences.

2. Memory Distortion and Decision Traps

In repeated decision-making scenarios, the peak-end rule—where agents remember only their most extreme (best or worst) experiences—drives the agent’s future policies, sometimes trapping them in suboptimal choices based solely on initial exposure (Mitsokapas et al., 2021). The transition probability for selecting an option is governed by a softmax/logit:

$$P^{+} = \frac{e^{\hat{U}^{+}/T}}{e^{\hat{U}^{+}/T} + e^{\hat{U}^{-}/T}}$$

where $\hat{U}^{\pm}$ is the peak utility and $T$ is the decision noise.

Early “lucky” high-utility events can cause agents to reinforce those choices even when their long-term expected utility is lower, unless sufficient exploratory noise ($T \sim 1/\lambda$ for exponential-tailed utilities) allows escape from the initial trap. Extreme-value theory and criticality analysis are used to show these phase transitions and persistent effects of early experiences.

3. Sensorimotor Integration in Biological Organisms

In minimal organisms such as C. elegans, early experiences are encoded as persistent molecular and synaptic states that structure sensorimotor responses. Experimental paradigms for salt chemotaxis demonstrate that pre-exposure (cultivation) to specific salt concentrations sets DAG levels and basal glutamate release in sensory neurons (ASER), thereby gating synaptic polarity upon future exposure (Vidal-Saez et al., 7 Feb 2024). Molecular dynamics (e.g., via cGMP, PKG, DAG) and equations regulating those processes define how past experience becomes a stable bias, directly controlling behavioral migration in salt gradients.

Table: Experience-Dependent Modulation Example (C. elegans)

Molecular State	Synapse Polarity	Behavioral Outcome
High DAG (after high salt)	Excitatory	Migration toward high salt
Low DAG (after low salt)	Inhibitory	Migration toward low salt

This suggests that even in simple neural circuits, early sensory experience irreversibly configures response pathways.

4. Predictive Coding and Early Attachment

Predictive coding frameworks extend the paradigm to social and emotional domains, notably in attachment theory. Early interactions with caregivers calibrate the weighting of prediction errors in the brain, with long-term consequences for affect regulation and interpersonal strategies (Lin, 10 Apr 2025).

For instance, the precision update rule: $$\mu_x \leftarrow \mu_x + \frac{\Pi_y}{\Pi_x + \Pi_y}(y - f(x))$$ shows that suppression of sensory precision ($\Pi_y$) following adverse early experiences leads to rigid, maladaptive internal models (e.g., avoidant or anxious attachment strategies). Chronic misweighting results in persistent free energy (unresolved surprise).

A plausible implication is that interventions for attachment-related challenges may seek to recalibrate these precision parameters, allowing for more flexible updating in response to new experience.

5. Lifelong Learning and Autonomous Agent Development

In artificial systems, the Early Experience Paradigm underpins frameworks for Experience-Driven Lifelong Learning (ELL) (Cai et al., 26 Aug 2025). Early agent-environment interactions produce trajectories ($\xi$) that populate persistent hierarchical memory ($\mathcal{K}$), drive skill abstraction, and support continual adaptation:

$a_t = \pi(o_t \mid \mathcal{K}_t)$

$$\mathcal{K}^{(i,k)} = \Phi_{\text{learn}}(\mathcal{K}^{(i,k-1)}, \xi^{(i,k)}, g^{(i)})$$

Benchmark environments (e.g., StuLife) formalize longitudinal, multistage evaluations to paper how early-life experiences build the knowledge and skill basis for future autonomous competence. This suggests that self-evolving agents require mechanisms to preserve, structure, and internalize foundational early data for robust, scalable growth.

6. Experience Replay in LLM Reasoning

Recent advances in reinforcement learning for LLMs leverage experience replay buffers that preferentially retain and exploit early successful or informative trajectories. ExGRPO organizes experiences by rollout correctness and entropy, filtering and replaying low-entropy, medium-difficulty samples to maximize learning signal (Zhan et al., 2 Oct 2025). Gains in sample efficiency and out-of-distribution performance demonstrate that judicious management of early experience data substantially shapes advanced reasoning abilities.

This supports an “Experience Paradigm” where the quality and curation of initial agent outputs are critical for subsequent scaling of reasoning performance in high-dimensional environments.

7. Reward-Free Learning and Self-Reflection

Agent-centric training paradigms posit that early, reward-free experiences—where future states serve as implicit supervision—build an essential intermediate foundation between imitation learning and full RL (Zhang et al., 9 Oct 2025). Through strategies such as implicit world modeling (predicting next state transitions) and self-reflection (generating rationales for expert vs. agent actions):

$$L_{IWM} = -\sum_{(s_i, a^j_i, s^j_i) \in D_{rollout}} \log p_\theta (s^j_i | s_i, a^j_i)$$

$$L_{SR} = -\sum_{(s_i, a^j_i, c^j_i) \in D_{refl}} \log p_\theta (c^j_i, a_i | s_i)$$

the agent confronts the full diversity of environment dynamics, learns robust policies, and generalizes better without exposure to explicit reward signals. This suggests that early exploratory interaction data provide dense feedback for learning environmental regularities and improve agent robustness and generalization.

8. Early Sensory Experience and Predictive Processing in Neural Networks

Cortical culture experiments reveal that deviance detection (DD)—a neural marker of predictive processing—emerges through both intrinsic circuit maturation and structured early sensory experience (Zhang et al., 1 Oct 2025). Statistical analyses show increased deviant response strength as networks self-organize toward near-critical dynamics (power-law avalanche distributions), and early “oddball” stimulation not only accelerates response latency but also modulates discrimination capacity.

Table: Impact of Early Oddball Experience

Network State	DD Strength	Response Latency
Near-critical (power-law)	High	Fast, precise
Subcritical/unstimulated	Moderate	Slower
Early oddball-trained	Lower discriminability	Shorter latency, duration

This informs artificial system design, suggesting that early input structure and network criticality can be tuned to optimize prediction sensitivity and circuit efficiency.

Conclusion

The Early Experience Paradigm constitutes a cross-disciplinary principle underscoring that initial experience—rewarding, salient, or self-generated—can fundamentally shape learning, decision-making, and adaptive capacity in both biological and artificial agents. Operationalized mathematically through eligibility traces, memory distortion, predictive coding, hierarchical skill learning, and experience replay, the paradigm draws a direct connection between early interaction, persistent behavioral or architectural bias, and the efficiency and generalizability of future adaptation. This compels the design of training protocols and agent architectures that preserve and strategically exploit the formative value of early data across domains and scales.