Training-Inference Alignment in RL

• Training-inference alignment in RL is a framework ensuring that policies trained in controlled environments match real-world deployment conditions to avoid performance drift.
• Techniques such as Mutual Alignment Transfer Learning and symbolic reward machine inference optimize sample efficiency and mitigate misalignment challenges in sim-to-real contexts.
• Practical insights include the importance of balancing auxiliary rewards, tuning alignment parameters, and normalizing embeddings to enhance policy robustness in dynamic settings.

Training-inference alignment for reinforcement learning (RL) refers to ensuring that the behaviors, distributions, and objectives encountered during RL model training are reliably matched to those encountered at inference (deployment) time. Misalignment between these phases can result in brittle, suboptimal, or even unstable policies—particularly acute when training is performed on proxy environments, reward functions, or data distributions that diverge from those present at deployment. The following sections systematically survey technical foundations, algorithmic advances, empirical methodologies, and open challenges in training-inference alignment for RL, with a focus on transfer and sim-to-real contexts, symbolic reward modeling, and emerging practical settings.

1. Foundations and Manifestations of Training-Inference Misalignment in RL

Training-inference misalignment in RL emerges when the distribution of states, transitions, or rewards experienced during learning differs from those encountered during inference or deployment. This can arise from:

• Domain gaps (e.g., simulation versus real robotics): Policies trained in simulation often encounter reality gaps due to unmodeled dynamics or sensor noise, leading to degraded real-world performance (Wulfmeier et al., 2017).
• Distributional shift: When the inference-time policy visits parts of the state space rarely or never seen during training, instability and performance collapse can occur.
• Proxy/auxiliary rewards and surrogate environments: Using dense heuristics or auxiliary tasks to aid learning introduces potential misalignment if these signals do not persist or transfer to deployment distributions.
• Symbolic training-inference decoupling: When a learning process builds an internal model of task structure (such as a reward machine) separate from environmental feedback, misalignment between the policy and inferred model may occur (Xu et al., 2019).

Misalignment manifests in increased sample complexity, spurious behaviors (e.g., "reward hacking" or overfitting), and suboptimal or unsafe deployment.

2. Algorithmic Approaches for Training-Inference Alignment

Several algorithmic strategies have arisen to mitigate training-inference misalignment:

Mutual Alignment Transfer Learning (MATL)

MATL jointly trains a simulator and real-world policy while introducing adversarial auxiliary rewards to align their state visitation distributions (Wulfmeier et al., 2017). The approach consists of:

Component	Description
Dual agents/policies	Sim and real-world policies trained in parallel, sharing state, action spaces
Discriminator D₍ω₎	Distinguishes simulation vs. real trajectories; policies seek to "fool" D₍ω₎
Auxiliary rewards	Robot: +log D₍ω₎(ζₜ); Simulator: –log D₍ω₎(ζₜ)
Joint reward signal	r(sₜ, aₜ) = env reward + λ × aux reward
Update scheme	Alternating rollouts, discriminator updates, policy TRPO updates

This scheme synchronizes distributions encountered during training and inference, reducing sim-to-real transfer failures and accelerating learning, especially under sparse or uninformative rewards.

Symbolic Model–Policy Joint Inference

Reward machines encapsulate non-Markovian or hierarchical reward structures as automata (Xu et al., 2019). By jointly inferring both the symbolic reward model and policy, agents align the learning and deployment reward computations and can transfer high-level structure between related tasks.

The process is iterative: collect counterexamples where the current reward machine disagrees with observed rewards, update the machine, and transfer Q-functions between old/new hypotheses, enhancing both exploration efficiency and transferability.

Embedding Alignment in Representation Learning

In dynamic or temporal RL settings, where learned embeddings might be used for downstream tasks, misalignment due to invariances (scale, rotation, translation) can confound inference (Gürsoy et al., 2021). Formal metrics—translation error ξ_tr, rotation error ξ_rot, scale error ξ_sc, stability error ξ_st—quantify misalignment. Aligning embeddings (e.g., by Procrustes analysis and normalization) improves prediction accuracy up to 90% (static) and 40% (dynamic) methods in real-world applications.

3. Empirical Evidence and Case Studies

MATL demonstrates sample efficiency improvements across a range of canonical tasks:

• On sparse-reward Cartpole/Cartpole Swingup/Reacher2D, MATL (matlf) reliably outpaces both independent training and fine-tuning on the real robot, achieving optimality in fewer robotic samples.
• In Hopper2D environments with only "falling cost" as the reward, MATL enables forward locomotion where independent RL yields no progress.
• Zero-reward real-world scenarios confirm that even with no environment reward signal, auxiliary alignment suffices for meaningful learning.
• Across different simulation engines (MuJoCo to DART), where dynamics change fundamentally, MATL maintains transferability, demonstrating robustness.

In joint inference of reward machines and policies, convergence is proven under minimality of reward machines and episode length sufficiency, and empirical results across benchmark "office world" and "Minecraft" tasks show consistent outperformance over flat or hierarchical RL baselines.

Alignment and stability analysis in representation learning (Gürsoy et al., 2021) further confirms, via both synthetic and real networks, that explicit alignment is necessary for effective downstream generalization.

4. Technical Challenges and Limitations

Notable challenges to robust training-inference alignment include:

• Adversarial training instability: The success of methods like MATL depends on stable discriminator optimization and careful tuning of alignment weight λ, which, if set incorrectly, risks either vanishing gradients (under-alignment) or conflicting reward signals (over-alignment).
• Reward signal conflict and safety: Auxiliary alignment rewards can drive agents into riskier, less conservative exploration if poorly balanced with environmental reward (e.g., in safety-critical tasks).
• Overfitting in the source domain: Generalization is undermined if simulation policies overfit to source dynamics, warranting caution in warm-starting or fine-tuning from strong source policies.
• Residual misalignment from symbolic model drift: In symbolic inference methods, insufficient or noisy counterexamples can prevent the reward machine from converging to the true underlying reward.

5. Generalization, Extensions, and Broader Implications

Mutual alignment frameworks and joint symbolic inference provide a template for aligning training and deployment in diverse RL settings:

• Generalization beyond robotics: Any RL scenario involving distributional mismatch between training and deployment—autonomous driving (sim→real), healthcare (synthetic→clinical), or industrial automation (lab→plant)—may benefit from mutual alignment/auxiliary reward mechanisms.
• Extended to higher-level knowledge and reasoning: Joint inference of symbolic task structure and reinforcement policy allows for rapid adaptation, curriculum learning, and more efficient transfer between structurally related tasks.
• Integration with downstream tasks: Embedding alignment in dynamic settings provides a practical preprocessing step for temporal prediction or inference tasks, ensuring that classifier or regressor boundaries learned on training data remain valid over time.

6. Outlook and Open Directions

Future directions include:

• More stable adversarial/objective-balancing schemes: Developing robust criteria for selecting alignment weights (λ) or dynamically adjusting them based on transfer progress or environment feedback.
• Incorporation with safe RL: Combining mutual alignment with policies explicitly designed for risk aversion or safety constraints.
• Automated, online symbolic model updates: Incremental approaches to reward machine inference could improve scalability to non-tabular or non-stationary environments.
• Alignment beyond RL: The alignment framework discussed here for RL readily extends to sequential prediction and generative modeling, where state or distribution misalignment can be similarly diagnosed and mitigated.

Careful consideration of training-inference alignment has become essential for the deployment of robust, sample-efficient, and safely transferable RL systems. The surveyed advances provide a principled toolkit for tackling distributional shift, credit assignment, and structural mismatch—central ingredients for trustworthy real-world RL deployment.