Self-Reward Functions for Autonomous Learning

Updated 9 February 2026

Self-reward functions are internally generated reward signals that enable autonomous agents to self-evaluate and guide their learning without external inputs.
They integrate with reinforcement learning loops by evaluating actions, updating policies through self-judgment, and often surpassing hand-crafted reward systems.
Empirical studies across robotics, language models, and vision tasks demonstrate improved learning efficiency, task performance, and robustness.

Self-reward functions are internal, learnable, or programmatically derived objective signals that enable autonomous agents—machine learning models or robots—to guide their own learning and improvement without reliance on external, environment-provided, or human-annotated rewards. This paradigm encompasses a spectrum from interpretable, modular self-evaluation in control and design, to complex self-judgment in LLMs, and meta-learned intrinsic rewards in reinforcement learning. Recent advances demonstrate that self-reward functions can drive the full training loop, match or exceed performance achieved by hand-crafted or external rewards, and are applicable in a variety of domains such as mathematical reasoning, robotics, text generation, and vision.

1. Mathematical Formulations and Canonical Self-Reward Functions

Self-reward functions are formally specified as $r: (x, a) \mapsto \mathbb{R}$ , where $x$ is typically a state, problem, or prompt and $a$ an agent’s output or action. The reward signal can be scalar binary, continuous, or vector-valued, depending on the context. Representative instantiations include:

LLM self-judging: A LLM with fixed parameters $\phi$ acts as a judge $J_\phi$ scoring generated outputs. For a problem $x$ and solution $a$ ,

$\hat{r}_\phi(x, a) = \begin{cases} 1, & \text{if } J_\phi \text{ judges } a \text{ correct given } x \ 0, & \text{otherwise} \end{cases}$

or more generally $\hat{r}_\phi(x, a) \in [0, 1]$ via a confidence score (Simonds et al., 12 May 2025).

Potential-based self-reward: Learned from offline data, a potential function $\Phi_\theta(s, g)$ is constructed such that

$\tilde{r}(s, a, s'; g) = r_{sparse}(s, a, s'; g) + \gamma \Phi_\theta(s', g) - \Phi_\theta(s, g)$

shaping reward to reflect progress toward goal $g$ (Mezghani et al., 2023).

Meta-learned intrinsic reward: The agent learns parameters $\phi$ for an internal reward $R^d_\phi(s,a)$ jointly with its policy,

$R_k(s, a) = R(s, a) + R^d_{\phi_k}(s, a) + B^{w_k}(s)$

where $B^{w_k}(s)$ is an exploration bonus. The reward is meta-learned to maximize expected extrinsic return (Devidze, 27 Mar 2025).

Internal model–based reward: Using Q-functions trained under teacher-forcing (text), construct a stepwise reward consistent with the Bellman equation:

$r(s, a) = f_\theta(s, a) - \max_{a'} f_\theta(s \oplus [a], a') \quad \text{[2210.08708]}$

Novel composite and calibrated rewards, such as dual-calibration for pseudo-label credibility and decisiveness-driven path shaping, further expand the self-reward function family (Tang et al., 20 Oct 2025, Han et al., 5 Sep 2025).

2. Integration in Reinforcement and Self-Improvement Loops

Self-reward functions are typically leveraged within policy optimization or reinforcement learning frameworks using standard algorithms (e.g., policy gradient, PPO, GRPO, Q-learning). The key workflow is:

Data generation: Sample a batch of inputs, states, or problems (possibly via synthetic generation).
Agent rollout: The agent samples actions/solutions $a_i \sim \pi_\theta(\cdot|x_i)$ according to its current policy.
Self-evaluation: Each $(x_i, a_i)$ is evaluated by the self-reward function, yielding rewards $\hat{r}_i$ .
Policy update: Compute a reward-advantage (e.g., $(\hat{r}_i - b)\nabla_\theta \log \pi_\theta(a_i | x_i)$ with a baseline $b$ for variance reduction), and perform a policy parameter update (Simonds et al., 12 May 2025).
(Optional) Reward parameter update: For meta-learned or trainable self-rewards, update reward parameters $\phi$ (outer loop), maximizing downstream task performance or self-consistency (Devidze, 27 Mar 2025, Mezghani et al., 2023).

This loop is universal across architectures: from LLMs using internal judges (Yuan et al., 2024, Simonds et al., 12 May 2025), to robotics via LLM-parameterized or self-aligned dense reward (Song et al., 2023, Zeng et al., 2024), to vision models refining themselves on self-judged datasets (Ghazouali et al., 2024).

3. Empirical Performance and Comparative Results

Quantitative assessments indicate that self-reward functions can dramatically improve learning speed, task success rates, and model efficiency:

Mathematical reasoning with LLM judges: On the MIT Integration Bee, self-judging RL (Qwen 2.5 7B judge; no ground-truth) improved solver accuracy from 35% (baseline) to 43% (+8%), surpassing GPT-4o (42%), and using a stronger generator plus GPT-4o judge reached 67% (baseline 50%, oracle 80%) (Simonds et al., 12 May 2025).
Offline robotics: Self-rewarding dense shaping (Go-FRESH) raised point-maze success from 35% (sparse RL) to 82% (learned potential-based reward), outperforming hand-crafted shaping (60%) (Mezghani et al., 2023).
Language modeling: Self-judging and self-consistency frameworks (Self-Rewarding LMs; SCIR) significantly improved alignment (AlpacaEval win-rates from ≈11% to 25–35%), and increased reward-model accuracy on held-out preferences from 55–65% to 75–85% (Yuan et al., 2024, Zhou et al., 13 Feb 2025).
Text-to-image: Class-conditional self-rewarding in diffusion models improved CLIP similarity by up to 8.7% and achieved a ≈60% win rate vs both open-source and commercial baselines (Ghazouali et al., 2024).

In reinforcement learning benchmarks, meta-learned self-reward architectures such as EXPLORS accelerated discovery in hard-exploration MDPs, reducing sample complexity from exponential to polynomial in problem size (Devidze, 27 Mar 2025).

4. Design Principles, Theoretical Guarantees, and Modes

Self-reward functions span multiple design strategies:

Fixed model-based self-judging: A frozen model assesses candidate outputs. Correctness (binary or confidence) is used directly as a reward signal (Simonds et al., 12 May 2025, Yuan et al., 2024).
Learned or meta-learned intrinsic rewards: The agent adapts internal reward networks to optimize for downstream extrinsic or self-supervised objectives, often using meta-gradient or inner-outer-loop procedures (Devidze, 27 Mar 2025, Hao et al., 2022).
Self-consistency and internal ensemble agreement: Aggregation of multiple self-rewarding models to stabilize and increase trustworthiness of self-generated preferences (e.g., combining DPO-style implicit reward and LLM-as-judge) (Zhou et al., 13 Feb 2025).
Potential-based and successor representation: Off-policy datasets are mined to construct dense progress-shaping or visitation-reward functions (Mezghani et al., 2023, Azad et al., 4 Jan 2025).
Composite and process-based rewards: Reward signals may combine outcome-based, path-based, confidence, and decisiveness metrics (e.g., DCAR and DPR in COMPASS) (Tang et al., 20 Oct 2025).
Explicitly interpretable modules: Architectures such as SRD encode semantics manually and provide neuron-level interpretability, with a self-labeled correctness signal used to train the entire system (Tjoa et al., 2021).

Theoretically, many self-reward constructs (e.g., SORS, potential shaping) are policy-invariant or preserve optimal policies under mild conditions, even in the absence of extrinsic rewards (Memarian et al., 2021). In meta-learned and self-aligned variants, conservative biasing (negative sampling) and self-consistency filtering ensure stable and reliable reward signals (Azad et al., 4 Jan 2025, Zhou et al., 13 Feb 2025).

5. Limitations, Open Research Challenges, and Extensions

Despite substantial progress, self-rewarding methods face significant limitations:

Reward hacking: Agents can exploit brittle or inaccurately specified reward models, often requiring careful prompt design or judge reinforcement. Small LLM judges are especially susceptible (Simonds et al., 12 May 2025).
Reward plateaus and judge capacity: When the judging model or criterion is fixed, agent improvement saturates at the judge's competence frontier. Teacher-student co-training or joint refinement can partially mitigate this (Simonds et al., 12 May 2025).
Signal noise and false positives/negatives: Imperfect judgments or reward misalignments introduce variance and retard convergence. Combining self-rewards with auxiliary heuristics or programmatic validation is a fruitful direction (Simonds et al., 12 May 2025, Tang et al., 20 Oct 2025).
Domain specificity and generalization: Some approaches depend heavily on the architecture or available observational modalities (e.g., vision, language, robotics), and may require adaptation or extension for broader applicability (Zeng et al., 2024, Ghazouali et al., 2024).
Reliability of composite/ensemble internal rewards: Discordance among multiple internal signals (e.g., DPO, LLM-as-Judge) can undermine alignment; self-consistency mechanisms help but may lead to data discard and efficiency bottlenecks (Zhou et al., 13 Feb 2025).
Interpretability: While modular or explicitly designed systems (SRD) provide high transparency, many learned self-reward functions remain opaque.

Relevant extension directions include continuous confidence calibration, joint reward-actor learning, multi-modal and multi-agent settings, curriculum generation, and more nuanced preference aggregation. Hybrid schemes combining self-training with lightweight human oversight are also actively explored (Simonds et al., 12 May 2025, Tang et al., 20 Oct 2025).

6. Application Domains and Illustrative Case Studies

Self-reward functions have broad applicability:

Domain	Self-Reward Mode	Key Results/Examples
LLMs	Self-judging LLM, SAR	8% accuracy gain in integration, >GPT-4o
Robotics	LLM-based self-refined	Zero-shot design, fast self-improvement; SR ≥96%
Text/image synthesis	Stepwise scores, CCSR	60%+ CLIP gain vs. strong baselines
RL hard-exploration	Meta-learned, SORS	Exponential→polynomial sample complexity
Control/interp. DNN	Modular, interpretable	Human-labeled weights, semantic transparency

In robotics, self-aligned and LLM-refined rewards have matched or exceeded manual expert-designed alternatives in both single-objective and multi-objective tasks(Zeng et al., 2024, Song et al., 2023). In vision, class-conditional self-rewarding mechanisms have enabled fully automated pipeline fine-tuning for text-to-image models, delivering substantial improvement without human labels(Ghazouali et al., 2024).

7. Theoretical and Cognitive Perspectives

Self-reward function research intersects with foundational questions in learning theory and cognitive science:

Compression of goal representations in human RL is theorized to move from working-memory–bound evaluation to an efficient compressed reward function, enabling rapid learning once rules are distilled—a phenomenon formalized and experimentally validated across multiple cognitive tasks (Molinaro et al., 8 Sep 2025).
Theory of mind and introspective self-reward: Models embedding self-aware subjective signals (e.g., "pain" beliefs) not only enhance exploration but can reproduce complex, humanlike relief-seeking or maladaptive behaviors, establishing a link between self-modeling and agent-driven skill acquisition (Petrowski et al., 6 Jan 2026).
Reward decomposition and independent obtainability: Decomposing complex environment rewards into independently obtainable self-reward factors improves modularity, transfer, and interpretability, aligning with both artificial and biological subgoal structures (Grimm et al., 2019).

A plausible implication is that as self-rewarding frameworks mature, agents may autonomously develop rich internal reward representations paralleling those seen in both human and animal learning systems, with potential for compositional transfer, continual improvement, and compact, human-auditable objectives.

References

Self-Rewarding Self Improving (Simonds et al., 12 May 2025)
Learning Goal-Conditioned Policies Offline with Self-Supervised Reward Shaping (Mezghani et al., 2023)
Exploration Through Introspection: A Self-Aware Reward Model (Petrowski et al., 6 Jan 2026)
Self-Aligned Reward: Towards Effective and Efficient Reasoners (Han et al., 5 Sep 2025)
Reward Design for Reinforcement Learning Agents (Devidze, 27 Mar 2025)
Teacher Forcing Recovers Reward Functions for Text Generation (Hao et al., 2022)
Self-Refined LLM as Automated Reward Function Designer for Deep Reinforcement Learning in Robotics (Song et al., 2023)
Learning Reward for Robot Skills Using LLMs via Self-Alignment (Zeng et al., 2024)
Rewarding the Journey, Not Just the Destination (Tang et al., 20 Oct 2025)
Self-Supervised Online Reward Shaping in Sparse-Reward Environments (Memarian et al., 2021)
Class-Conditional self-reward mechanism for improved Text-to-Image models (Ghazouali et al., 2024)
Self-Consistency of the Internal Reward Models Improves Self-Rewarding LLMs (Zhou et al., 13 Feb 2025)
Self Punishment and Reward Backfill for Deep Q-Learning (Bonyadi et al., 2020)
SR-Reward: Taking The Path More Traveled (Azad et al., 4 Jan 2025)
Learning Independently-Obtainable Reward Functions (Grimm et al., 2019)
Reward function compression facilitates goal-dependent reinforcement learning (Molinaro et al., 8 Sep 2025)
Self-Rewarding LLMs (Yuan et al., 2024)
Self Reward Design with Fine-grained Interpretability (Tjoa et al., 2021)