Competence-Aware Scheduling Techniques

• Competence-aware scheduling is a methodology that explicitly models an entity’s evolving proficiency to dynamically optimize task assignments and resource allocation.
• It integrates techniques like dynamic curriculum reordering, Bayesian feedback, and value-density assessments to tailor schedules in instruction tuning, robotics, and teleoperation.
• By aligning task difficulty with current capabilities, competence-aware scheduling enhances efficiency, accelerates convergence, and reduces operational costs compared to static approaches.

Competence-aware scheduling is a class of scheduling methodologies in which an agent’s, model’s, or operator’s current proficiency is explicitly modeled and exploited to drive online or curriculum-based scheduling decisions. Rather than relying on static task difficulty orderings or hardcoded skill assumptions, competence-aware scheduling continuously estimates and adapts to the entity’s evolving capabilities. This enables more efficient resource allocation, training, or task assignment, with demonstrated benefits over heuristic and rigid approaches in diverse settings such as instruction tuning for LLMs, mixed-autonomy robotics, and teleoperation assignment problems (Li et al., 17 Sep 2025, Basich et al., 2020, Rosemarin et al., 2017).

1. Formal Definitions and Theoretical Foundation

Competence-aware scheduling frameworks share two defining characteristics: (i) an explicit representation of competence for each task or autonomy level and (ii) a scheduling policy that adapts in real-time to updates in this competence estimate.

• In curriculum learning for LLM instruction tuning, “competence” is measured by dynamic data-driven metrics such as model loss or an adversarially trained out-of-distribution detector, alongside heuristic task-wise scores. For example, in CAMPUS (“Competence-Aware Multi-Perspective cUrriculum inStruction tuning”), every training example $x$ is assigned multiple difficulty scores $d_1(x),...,d_n(x)$, some static (e.g., token length), others competence-aware (e.g., current loss under model parameters $\theta$) (Li et al., 17 Sep 2025).
• In mixed-autonomy systems, Basich et al. define a Competence-Aware System (CAS) as a generalization of the Stochastic Shortest Path (SSP) problem, where the state space includes both environment and autonomy levels $L=\{l_0, ..., l_n\}$, and agent competence at each $(\text{state}, \text{action})$ is learned via Bayesian updates from human feedback (Basich et al., 2020).
• In teleoperator-aware scheduling, task assignment is governed by operator-dependent efficiency ($\eta^i_\tau$) and realized value ($\psi^i_j$), leading to a competence-aware value-density for each $(\text{job}, \text{operator})$ pair, which in turn drives the online scheduler (Rosemarin et al., 2017).

2. Methodological Approaches

Curriculum Learning and CAMPUS Framework

CAMPUS formalizes multiple parallel curricula, each sorted according to a different difficulty metric (both static and competence-aware). At every training iteration:

1. Shard expansion: Each curriculum $D_i$ is divided into $T$ ordered “shards,” with the $j$-th shard $S_i(t_i)$ exposed based on a growth equation: $s(t)=\min(1,[t\frac{1-s(1)^p}{T}+s(1)^p]^{1/p})$.
2. Competence measurement: Model perplexity $\mathrm{PPL}(S_i)$ is computed for each shard.
3. Dynamic selection: The next training batch is drawn from the shard with lowest perplexity, i.e., that which the model is most competent on: $j = \arg\min_{i=1..n}\mathrm{PPL}(S_i(t_i))$.
4. If the difficulty metric is competence-aware, the affected shard is dynamically re-sorted based on the current $\theta$ after training—creating a closed feedback loop (Li et al., 17 Sep 2025).

Autonomy Optimization in CAS

Competence in a CAS is updated online by observing outcomes and human feedback for actions taken at different autonomy levels. The system maintains for each $(s, l, a, l')$ a vector of feedback counts, updated via a Dirichlet-multinomial model:

$$\lambda_t(\sigma|s,l,a,l') = \frac{N_t(s,l,a,l',\sigma) + \alpha_0}{\sum_{\sigma'} N_t(s,l,a,l',\sigma') + \alpha_0}$$

The optimal scheduling policy is recovered by embedding this structure into a value-iteration solver over the factored state-action space, with exploration over the autonomy hierarchy gated by safety constraints and human permissions (Basich et al., 2020).

Online Operator-Aware Scheduling

In the teleoperator assignment setting, the online problem is formulated as a maximum-value scheduling LP, with realized value density for each assignment as:

$$\dot\rho^i_j = \rho_j \psi^i_j \eta^i_{\tau_j} = \frac{v_j \psi^i_j \eta^i_{\tau_j}}{p_j}$$

Scheduling decisions (possibly preemptive) are made greedily based on $\dot\rho^i_j$, with competitive ratio maintained by dual-fitting analysis. A learned comparator network (cmpNN) is trained using optimal assignments from small-scale offline instances to make pairwise comparisons for both (job, operator) and (operator, job) decisions online (Rosemarin et al., 2017).

3. Algorithmic Structures

The core algorithmic motifs of competence-aware scheduling are summarized below:

Setting	Competence metric(s)	Scheduling mechanism	Adaptive process
Instruction tuning (Li et al., 17 Sep 2025)	Loss, OOD score, heuristics	Curriculum shard selection	Dynamic reordering/shard growth
Mixed autonomy (Basich et al., 2020)	Bayesian feedback aggregation	Value-iteration MDP	Online model update + gating
Operator assignment (Rosemarin et al., 2017)	Operator competence, efficiency	Value-density + comparator	ML comparator ranking/shifts

Competence-aware methods implement either explicit selection (e.g., pick shard/operator/action with highest estimated competence or value) or adapt inputs to the scheduler through an online update (e.g., competence-aware reordering of curricula, Bayesian parameter adaptation).

4. Empirical Results and Benefits

Empirical evaluations across domains demonstrate the concrete benefits of competence-aware scheduling:

• Instruction tuning: CAMPUS achieves approximately a 7% performance increase over static curriculum, random-shuffle, and recent baselines. It converges faster, achieves lower training loss versus data seen, and displays enhanced resistance to catastrophic forgetting across heterogeneous task mixes. Single static metrics or sequential sub-curriculum scheduling exhibit inferior performance (Li et al., 17 Sep 2025).
• Mixed-autonomy robotics: CAS in campus robot and autonomous driving scenarios reaches ≈100% and ≈90% level-optimality, respectively, within a few hundred episodes, with substantial reductions in human intervention and cost (up to 50% cost reduction in some settings) (Basich et al., 2020).
• Teleoperator-sensitive scheduling: In both small (40 jobs × 4 servers) and large (1000 jobs × 100 servers) scenarios, the learned Ranking algorithm achieves 5–15% higher realized value than the value-density baseline, with greater gains as operator job affinity increases. Runtime remains highly parallelizable and scalable to real-world environments (Rosemarin et al., 2017).

5. Comparative Advantages over Static and Heuristic Schedules

Competence-aware scheduling outperforms static or heuristic-only approaches by continuously aligning scheduling with an agent’s “zone of proximal development”:

• Static orderings may force sub-optimal learning or task allocation, especially for heterogeneous agents or models whose initial competence varies by domain.
• By measuring model or operator proficiency online and feeding that estimation back into the scheduler, these methods steer training or resource use toward instances that maximize marginal learning or utility.
• Dynamic curriculums and autonomy strategies are robust to heterogeneity, mixing, and evolving task distributions, and empirically mitigate phenomena such as catastrophic forgetting and inefficient preemption.

6. Limitations and Open Challenges

Current competence-aware scheduling approaches are subject to several structural and conceptual limitations:

• Fairness and starvation avoidance: Many frameworks maximize aggregate utility but do not guarantee per-agent fairness or task starvation avoidance; this is noted as an open problem in operator scheduling (Rosemarin et al., 2017).
• Complexity versus deployability: While comparator networks scale efficiently, richer models that capture complex dependencies, dependencies between operators and jobs, or more nuanced notions of competence may pose computational or data requirements.
• Ethical and safety considerations: Particularly in human-in-the-loop or assistive robotics, ethical constraints (e.g., avoidance of overburdening human operators, prioritization under conflicting objectives) require principled integration beyond technical optimality.
• Dynamic skill progression: The adaptation of operator or model skills during deployment ("upskilling") and its interaction with competence-aware scheduling remains largely unaddressed.

7. Future Directions

Expanding competence-aware scheduling involves several promising avenues:

• Integration with richer curriculum design, including task dependencies, adaptive weightings, and transfer learning across competence profiles.
• End-to-end frameworks in mixed-human–machine systems that unify competence estimation, bounded-exploration, and objective-aligned reward shaping.
• Incorporation of safety, ethical, and fairness guarantees into the scheduling logic, particularly in high-stakes and autonomous domains.
• Automated discovery and meta-learning of the most informative competence metrics for a given application or agent class.

Competence-aware scheduling thus represents a foundational paradigm for adaptive, efficient instruction, task assignment, and autonomy management, with substantiated empirical and theoretical benefits across machine learning and robotics (Li et al., 17 Sep 2025, Basich et al., 2020, Rosemarin et al., 2017).