Multi-tasking: Verifiable & Non-verifiable Rewards

• Multi-tasking with Verifiable and Non-verifiable Rewards involves training AI agents, like RL or LLMs, to optimize multiple objectives using both objective, checkable rewards and subjective, human-based feedback.
• Advanced frameworks employ techniques like constrained policy coupling, reward function decomposition, and distributional inference to integrate diverse reward signals and promote robustness.
• Integrating mixed reward types presents challenges in reward structure, aggregation, and robustness to noise, but is crucial for applications such as dialog management, generalist reasoning, and integrating human feedback effectively.

Multi-tasking with Verifiable and Non-verifiable Rewards refers to the development, training, and deployment of learning systems—particularly reinforcement learning (RL) agents and LLMs—that are capable of optimizing for multiple objectives simultaneously, where these objectives are associated with reward signals that may be objectively verifiable (e.g., ground-truth correctness) or fundamentally non-verifiable (e.g., human preference, style, or open-ended quality). This paradigm is prominent in situations where agents operate in complex, heterogeneous environments or must integrate diverse forms of feedback across various tasks. Research in this area is motivated by challenges in reward specification, robustness, generalization, and the practical integration of conflicting or incomplete reward sources.

1. Foundations and Definitions

Verifiable rewards are those generated by deterministic, objective mechanisms—such as rule-based or algorithmic checkers, reference answers, or physical measurements—that yield unambiguous correctness signals. Typical examples include mathematical correctness, code test cases, or bounding-box overlap in vision tasks. In contrast, non-verifiable rewards encompass forms of supervision derived from human judgments, preferences, or heuristics, which may be inherently subjective, ambiguous, or context-dependent, and are not unambiguously checkable against a reference.

Multi-tasking in RL extends traditional single-task approaches to settings where agents must learn across a suite of tasks, each associated with potentially distinct reward signals of varying verifiability. This creates requirements for generalization, transfer, and robustness, especially when simultaneously facing reward misspecification, partial observability, or reward conflicts.

2. Multi-task Learning Frameworks for Mixed Rewards

Recent methodological advances emphasize architectures and optimization schemes that enable effective joint training with both verifiable and non-verifiable rewards. Several frameworks illustrate core strategies:

• Constrained Policy Coupling: Cross-learning constrains task-specific policies to remain within a neighborhood (radius ε) of a shared central policy, promoting information sharing among tasks (2008.11895). Optimization is typically achieved via projected policy gradient methods, with constraints enforced in the policy space. This supports generalization and adaptation in environments where some task rewards may be stochastic or only partially verifiable.
• Multi-Task Reward Function Decomposition: Tasks with reward functions of the form $$r_t(s, a, s') = \bar{r}_t(s, a, s') + r_{CS}(s, a, s')$$ (task-specific, verifiable + shared, non-verifiable common-sense) can benefit from multi-task inverse RL, which disentangles generalizable, environment-level rewards from task-specific signals (2402.11367). Simultaneous training on diverse tasks prevents spurious reward learning tied to individual tasks and encourages better transfer.
• Distributional Reward Inference: Approaches like Multitask Inverse Reward Design (MIRD) propagate uncertainty over the true reward function by combining multiple potentially misspecified or conflicting sources (2103.12142). By maintaining a posterior over possible rewards, agents avoid overcommitting to any potentially flawed input; this is critical when reward verifiability varies across sources.
• Variational Multi-task IRL: Variational Inverse Reinforcement Learning introduces mutual-information based empowerment regularization within a generative adversarial (GAIL) framework, enabling the learning of reward functions that are both transferable (across task compositions) and robust to non-verifiable or unlabeled expert demonstrations (2206.09498).

3. Reward Integration, Adaptation, and Robustness

Combining verifiable and non-verifiable rewards in a multi-task context introduces significant challenges:

• Reward Structure and Propagation: Joint training often uses adaptive weighting or aggregation functions, such as weighted sums or dynamically learned weights reflecting task uncertainty (2506.09183). In agentic reward modeling, human preference models (base RM) are combined with aspect-specific verifiable signals (e.g., factuality, instruction-following), with modular routers selecting which verifiers to apply (2502.19328).
• Trade-offs: Informativeness vs. Conservatism: Reward aggregation must navigate a trade-off between being informative (enabling decisive action when sources agree) and conservative (retreating to broader behavior distributions when sources conflict), as formally analyzed in MIRD (2103.12142). Highly concentrated posteriors promote efficiency but risk catastrophic errors from misspecification; broad posteriors preserve option value but may hinder performance.
• Robustness to Noisy or Misspecified Rewards: Multi-tasking frameworks such as cross-learning and MT-CSIRL are designed for robustness to reward noise and partial observability. By enforcing structural or parametric coupling between tasks, these frameworks allow for reliable adaptation even when some tasks provide only non-verifiable or sample-based feedback.

4. Practical Applications and Empirical Findings

Multi-tasking with heterogeneous reward sources is pivotal in a range of domains:

• Dialog Management: Hierarchical, multi-level reward modeling decomposes contributions at domain, act, and slot levels, with partial rewards enabling learning from imperfect or partially verifiable dialog acts (2104.04748).
• Generalist Reasoning and Skill Transfer: Procedural generation of tasks and verifiable checkers (e.g., Reasoning Gym (2505.24760), Enigmata (2505.19914)) enables large-scale training and evaluation in environments where reward signals are both diverse and fully verifiable. Empirical results show significant knowledge transfer across tasks, strong generalization, and efficiency gains with principled reward modeling.
• Integrating Human Feedback: Pairwise generative reward models (GenRM) transform subjective writing preferences into more reliable, quasi-verifiable reward signals for creative tasks, facilitating robust RLVR even in the absence of ground-truth answers (2506.00103). Bootstrapped RL algorithms use internal rollouts as temporary references for dynamic, reference-free optimization.
• Verification and Self-Verification: The integration of verifiable rewards with self-verification objectives enhances reasoning accuracy and introspective capabilities in LLMs, as demonstrated by simultaneous policy updates for task-solving and self-critique (2505.13445).

5. Open Challenges and Future Directions

Despite substantial progress, several challenges persist:

• Verifier Limitations and Reward Hacking: Rule-based verifiers are prone to false negatives for semantically equivalent outputs, while model-based verifiers risk false positives and reward hacking (2505.22203). Hybrid systems combining discriminative, adversarially-trained verifiers with rule-based filters are promising immediate remedies.
• Multi-tasking in Realistic Settings: As models scale to more diverse tasks—some with objective correctness criteria, others with subjective quality—a unified framework requires dynamic weighting, composable reward architectures, and robust multi-objective optimization (2506.09183, 2505.10218).
• Benchmarking and Calibration: The emergence of reference-based reward benchmarks (e.g., VerifyBench (2505.15801)) provides standardized assessment for verifiable reward systems, but also highlights consistent gaps in difficult or ambiguous cases, emphasizing the importance of continual improvement in reward modeling and verification strategies.
• Application to Multimodal and Open-ended Domains: Approaches such as SATORI decompose multimodal reasoning into explicit, verifiable subtasks to anchor rewards in measurable elements (e.g., captioning, region localization, answer accuracy), reducing RL variance and improving interpretability (2505.19094).

6. Summary Table: Key Frameworks and Principles

Aspect	Technique/Approach	Application Context
Multi-task Policy Coupling	Cross-learning Constraints	Robust adaptation, partial observability
Reward Fusion	Posterior/Mixture Modeling	Handling reward misspecification, robustness
Decomposed Reward Modeling	Hierarchical/Sequential FL	Dialog management, interpretable reward composition
Human Preference Integration	Pairwise GenRM, ARM	Creative writing, instruction following, open QA
Adversarial-robust Verification	Discriminative/Fallback Hybrid	Complex reasoning, reward hacking prevention
Empirical Benchmarks	VerifyBench, Reference Tasks	Systematic evaluation, multi-task RL validation

7. Implications and Outlook

Multi-tasking with both verifiable and non-verifiable rewards is foundational to the development of scalable, robust, and generalist AI systems. Aligning policies to diverse objectives—some objectively checkable, others rooted in human judgment—requires principled integration of heterogeneous signals, careful design of reward aggregation and adaptation, and ongoing benchmarking. The challenge of reward misspecification, transfer, and robustness will remain central as systems are deployed in increasingly complex and open-ended real-world environments. Research continues to progress towards unified frameworks that flexibly and safely integrate all available feedback channels for effective multi-task RL and model alignment.