Reinforcement Learning from Task Feedback (RLTF)

• RLTF is a learning framework that leverages various forms of task feedback instead of traditional reward functions, enabling flexible and scalable policy optimization.
• It mitigates issues like reward sparsity and misspecification by utilizing structured signals such as temporal, comparative, and symbolic feedback.
• RLTF strategies integrate techniques like reward shaping, preference modeling, and value estimation to achieve state-of-the-art performance in domains like robotics, NLP, and program synthesis.

Reinforcement Learning from Task Feedback (RLTF) is a paradigm in which an agent learns to optimize behavior not from an explicit, manually specified reward function, but from feedback that directly encodes information about task performance or objectives, potentially in a variety of forms. This task feedback may consist of scalar or structured signals generated by humans, automated evaluators, symbolic systems, or other external entities. RLTF generalizes the concept of interactive and preference-based reinforcement learning, including both supervised and reinforcement learning modalities, and is increasingly seen as a scalable solution to the longstanding reward specification and credit assignment problems in real-world and complex artificial environments (Shu et al., 26 May 2025).

1. Foundations and Motivation

In standard reinforcement learning (RL), the agent is provided a reward function $r(s,a)$ that evaluates each state-action pair, or occasionally, each transition or trajectory. However, this requirement is often impractical in real environments due to the need for costly engineering, risk of reward misspecification, and prevalence of sparse or delayed feedback. RLTF remedies this issue by enabling agents to learn from task-related feedback that may be intermittent, global (e.g., trajectory- or episode-level), comparative, or structured (e.g., preferences or certificates), thus bypassing the need for dense, per-step reward signals (Efroni et al., 2020, Kong et al., 2023, Shu et al., 26 May 2025).

Forms of task feedback encountered in RLTF include:

• Temporal and trajectory-level feedback: Rewards, ratings, or scores associated with entire episodes or segments (Efroni et al., 2020).
• Comparative/judgmental feedback: Preferences or rankings between pairs of trajectories (Alinejad et al., 17 Oct 2025).
• Human/AI-provided evaluative signals: Absolute or relative ratings, scores, or critiques (Luu et al., 15 Jun 2025, Yu et al., 2023).
• Symbolic or automaton-derived feedback: Program certificates, logical proofs, or automaton-based rankings providing fine-grained signal under domain-specific constraints (Jha et al., 26 May 2024, Alinejad et al., 17 Oct 2025).

These mechanisms collectively mitigate reward sparsity and misalignment, facilitate learning in environments with non-Markovian or history-dependent objectives, and enable sample-efficient alignment with complex task specifications.

2. Formal Problem Class and Task Feedback Models

RLTF generalizes the standard Markov Decision Process (MDP) and Partially Observable MDP (POMDP) frameworks by relaxing the assumption that the agent has access to an oracle per-timestep reward. Instead, the feedback model can be characterized as follows (Efroni et al., 2020, Alinejad et al., 17 Oct 2025, Jha et al., 26 May 2024):

• MDP Formulation: $\mathcal{M} = (\mathcal{S}, \mathcal{A}, P, r, H)$, where reward $r$ is hidden or replaced by a feedback function $f$.
• Feedback Function: Task feedback is available as a function $F(\xi)$, where $\xi$ may be a (state, action), a trajectory $\tau$, or a pair $(\tau_1, \tau_2)$.
• Preference-based feedback: Task feedback can encode ordering or ranking over trajectories, facilitating learning when the true reward is non-Markovian or non-decomposable (Alinejad et al., 17 Oct 2025).
• Certificate-based or token-wise feedback: In applications such as code synthesis or reasoning, symbolic tools produce token-level certificates that provide dense, informative supervision over structured outputs (Jha et al., 26 May 2024).
• Scalar ratings and noisy or multi-distributional feedback: Scalar human or AI-provided ratings, possibly with nontrivial noise or ambiguity, are converted into training signals using robust signal processing methods (Yu et al., 2023, Luu et al., 15 Jun 2025).

Under RLTF, the objective is to learn a policy $\pi^*$ maximizing expected cumulative feedback-derived utility, which may be articulated as maximizing the expected return aggregated from feedback signals or as minimizing a loss derived from preference inconsistencies or token-level error certificates.

3. Methods for Policy and Reward Model Learning from Task Feedback

A broad range of frameworks have been developed for RLTF, differing in the structure of feedback, temporal granularity, and integration with RL algorithms:

• Dense reward shaping from temporal feedback (RFTF): Dense, episode-level or temporal feedback is transformed into per-step reward signals via value modeling, re-normalization, and reward shaping. The RFTF approach fits a value head to successful demonstration trajectories using pairwise contrastive loss and then derives dense incremental rewards as the increase in discounted state value. Generalized Advantage Estimation (GAE) is used for credit assignment, with sample balance and PPO-style clipped surrogate objectives to stabilize optimization (Shu et al., 26 May 2025).
• Automated or human preference modeling: Sampling trajectories and generating pairwise preferences (manually or via automata); a reward model is then trained using ranking or hinge loss. This accommodates both static (batch) and dynamic (iterative) preference collection and reward model optimization (Alinejad et al., 17 Oct 2025, Kong et al., 2023).
• Symbolic feedback and token-wise rewards (RLSF): Symbolic reasoners generate certificates that localize errors in model outputs; dense, token-level reward vectors are computed for precise credit assignment. This paradigm improves sample efficiency in program synthesis and logical reasoning tasks (Jha et al., 26 May 2024).
• Rating-based RL from vision-LLM (VLM) feedback (ERL-VLM): Segments of agent experience are rated by AI teachers (VLMs) with Likert-scale scores; rating-based probabilistic models (e.g., MAE loss and stratified sampling) are used to learn stable reward models, which are then relabeled back into the RL process for off-policy optimization (Luu et al., 15 Jun 2025).
• Scalar and binary (noisy) feedback normalization: Multimodal or noisy teacher feedback is de-noised and confidence-weighted via statistical modeling or classifier-based filtering, improving stability and robustness (Li et al., 23 Sep 2024, Yu et al., 2023).
• Interactive feedback via guided exploration or policy shaping: Early-phase intervention by advisors (human or artificial) directly overrules actions to guide exploration, with later removal of intervention (Moreira et al., 2020, Harnack et al., 2022).

The resulting reward or advantage signals are then integrated with canonical RL updates—typically PPO, SAC, actor-critic, or policy-gradient schemes—with modifications to handle dense, sparse, or delayed returns from heterogenous feedback.

4. Empirical Results and Benchmark Domains

RLTF methods have demonstrated state-of-the-art performance and significant sample efficiency gains across diverse domains:

Domain	Feedback Modality	RLTF Method(s)	Empirical Highlights	Reference
Embodied agents (CALVIN)	Temporal task feedback, dense value model	RFTF	SOTA avg. success length 4.296, rapid adaptation	(Shu et al., 26 May 2025)
LLM alignment/NLP tasks	Automated composite rewards (multi-dim.)	REFINE-AF	63–66% task improvement over SFT	(Roy et al., 10 May 2025)
Code synthesis (APPS, MBPP)	Unit test (multi-granular) feedback	RLTF (unit-test), RLSF	Pass@1 up to 1.45% APPS, RLSF: CompAcc 63.95%	(Liu et al., 2023, Jha et al., 26 May 2024)
Robotics (RLBench, MetaWorld)	LLM/VLM-generated absolute or binary ratings	Lafite-RL, ERL-VLM	RLBench: 70.3% push button, MetaWorld: >80% success	(Chu et al., 2023, Luu et al., 15 Jun 2025)
Continuous robot control	Interactive exploratory feedback	CACLA (tuned feedback)	Feedback schedules accelerate learning, stabilize	(Harnack et al., 2022)
Preference-based grid/world tasks	Automaton-based trajectory preferences	RLAF	Outperforms reward machines, LTL-based baselines	(Alinejad et al., 17 Oct 2025)
Interactive human feedback	Binary/scalar, noisy or distributional	STEADY, CANDERE-COACH	STEADY: +5.12 avg. return gain, CANDERE: up to 40% noise tolerance	(Yu et al., 2023, Li et al., 23 Sep 2024)

Typical evaluation metrics include average episodic return, pass rates, success rates, reward per cumulative steps, and improvements over supervised or RLHF-style baselines.

5. Theoretical Guarantees and Practical Considerations

Several RLTF approaches offer nontrivial theoretical guarantees:

• Feedback-efficient active RL: Under structured reward classes (e.g., low eluder dimension), active pool-based feedback and bonus-driven querying achieve $\widetilde{O}(H \dim_R^2)$ sample complexity for near-optimality, independent of $\epsilon$ and transition complexity (Kong et al., 2023).
• Trajectory-level regret bounds: When only trajectory feedback is given, least-squares reward estimation plus optimism or Thompson-sampling yields regret upper bounds scaling as $O(SAH\sqrt{K})$ (Efroni et al., 2020).
• Preference-based convergence: Under persistent exploration and consistent preference generation, automaton-guided RLTF guarantees $\varepsilon$-optimality with respect to the true non-Markovian objective (Alinejad et al., 17 Oct 2025).
• Noise tolerance: For label-flip noise in binary or scalar teacher feedback, algorithms like CANDERE-COACH recover near-optimal performance up to 40% feedback noise rates, provided adequate denoising and classifier calibration (Li et al., 23 Sep 2024, Yu et al., 2023).

Limitations and open considerations include the scalability of preference generation (quadratic in pairs), reliance on well-specified feedback modalities, prompt and reward model engineering for VLM/LLM-based pipelines, and the need for more advanced feedback calibration and scheduling (adaptive rates, online calibration, confidence estimation).

6. Extensions, Applications, and Outlook

RLTF has established itself as a unifying framework across domains such as robotics, natural language processing, program synthesis, and logic-constrained reasoning:

• In high-dimensional or non-Markovian environments, automaton-derived and symbolic feedback provide a mechanism to enforce and generalize complex temporal and logical constraints (Alinejad et al., 17 Oct 2025, Jha et al., 26 May 2024).
• VLM- and LLM-generated feedback enables scalable, low-cost deployment of RLTF systems by reducing or eliminating reliance on human annotators (Chu et al., 2023, Luu et al., 15 Jun 2025).
• Interactive learning scenarios, including those with noisy or variable-quality feedback from non-experts, have improved robustness through model-based denoising or confidence-adaptive scalar rewards (Li et al., 23 Sep 2024, Yu et al., 2023).
• RLTF methods have been applied to rapid domain adaptation, code and instruction generation, robotic manipulation, and benchmarking of AI alignment protocols with both synthetic and human-like feedback loops (Roy et al., 10 May 2025, Liu et al., 2023).

A plausible implication is that RLTF methods will continue to subsume previous paradigms in both human-in-the-loop and AI-teacher alignment pipelines, especially as research addresses remaining challenges in feedback efficiency, multi-modality, and theoretical characterization for complex objectives.