Reinforcement Pre-Training (RPT)

• Reinforcement Pre-Training is a paradigm that redefines neural pre-training by integrating reinforcement learning objectives with supervised signals.
• It reformulates tasks, such as next-token prediction and image transformation, as reward-maximization problems to enhance reasoning and control.
• RPT yields improved sample efficiency and robust, transferable skills across language, vision, and sensorimotor domains.

Reinforcement Pre-Training (RPT) is an overarching paradigm that leverages reinforcement learning (RL) methods and objectives to pre-train neural networks, preceding or supplanting conventional supervised pre-training. RPT is deployed both in LLMing and embodied domains (e.g., robotics, vision) to accelerate task learning, improve generalization, and bridge the gap between standard pre-training and RL fine-tuning. Recent advances reframe pre-training itself as an RL problem, integrate supervised and RL-style reward signals, and emphasize the inductive transfer of reasoning or behavioral capabilities as a scaling mechanism.

1. Theoretical Foundations and General Principles

RPT originates at the intersection of representation learning and RL, motivated by the sample inefficiency and limited generalization of tabula rasa RL agents (Xie et al., 2022). It operationalizes pre-training via RL-based objectives, using rewards that are either external (ground-truth), intrinsic (e.g., curiosity, diversity), or automatically verifiable. Unlike traditional pre-training focused exclusively on supervised next-token prediction or classification, RPT recasts learning as the maximization of expected rewards over trajectories that encode reasoning, perception, or control processes.

Mathematically, a canonical RPT objective maximizes expected reward, as in RL: $$J(\pi_\theta) = \mathbb{E}_{\pi_\theta, \mathcal{T}, \rho_0}\left[ \sum_{t=0}^{\infty} \gamma^t r(s_t, a_t) \right]$$ but with environment, reward, and action structure engineered to facilitate pre-training on large unlabeled or weakly labeled data (Dong et al., 9 Jun 2025, Ghosh et al., 13 Jun 2025).

2. Methodologies and Objective Formulations

2.1 Language and Next-Token Reasoning

In LLMs, RPT reframes next-token prediction as a reasoning task, where at each step the model generates a rationale (a chain-of-thought, $c_t$) before committing to a token prediction $y_t$: $$o_t = (c_t, y_t) \sim \pi_\theta(\cdot \mid x_{<t})$$ A verifiable reward $r_t$ is automatically computed, e.g.,

$$r_t = 1 \text{ if } y_t = x_t; \quad r_t = 0 \text{ otherwise}$$

The RL objective is to maximize the expected reward for correct next-token predictions: $$\mathcal{J}_\text{RPT}(\theta) = \mathbb{E}_{(x_{<t},x_{\geq t})\sim\mathcal{D}}\mathbb{E}_{o_t\sim\pi_\theta(\cdot \mid x_{<t})}\left[ r_t \right]$$ This contrasts with standard next-token log-likelihood objectives and enables “on-policy” reasoning and credit assignment during pre-training (Dong et al., 9 Jun 2025).

2.2 Visual and Sensorimotor Domains

For visual foundation models, RPT models pre-training as value-based RL over transformations (e.g., image crops), associating states (views), actions (transformations), and rewards (semantic annotations or proxy scores) (Ghosh et al., 13 Jun 2025): $$Q^*(x, a, \ell) = \mathbb{E}_{x' \sim P(\cdot | x, a)}\left[ r(x', \ell) + \gamma \max_{a'} Q^*(x', a', \ell) \right]$$ The Q-function propagates annotation-driven rewards through a chain of augmentations, paralleling bootstrapping in classic RL.

In robotics, RPT typically operates over sensorimotor sequences. Transformer-based encoders mask and reconstruct multi-modal tokens (images, proprioception, actions) with high masking ratios, encouraging the learning of structured, predictive physical world models (Radosavovic et al., 2023). These models are evaluated by success rate on downstream manipulation, generalization across laboratories, robots, and tasks.

3. Empirical Effects and Scaling Laws

3.1 LLMing

RPT-trained LLMs achieve superior next-token prediction accuracy—most notably on high-entropy prediction “hard” tokens—relative to comparably sized models trained solely by supervised losses (Dong et al., 9 Jun 2025). Scaling curves show predictable, power-law improvement in accuracy as pre-training compute increases: $P(C) = \frac{A}{C^\alpha} + P^*$ where $P(C)$ is next-token accuracy at compute $C$; this scaling law is robust and matches or surpasses standard pre-training approaches.

3.2 Visual and Multimodal Representations

When pre-training is formulated as value-based RL, as in annotation bootstrapping for vision, learned representations outperform standard crop-invariant consistency baselines (e.g., SimCLR, DINO, CLIP) across non-object-centric, highly cluttered, or weakly labeled datasets (e.g., COCO, EpicKitchens, CC12M) (Ghosh et al., 13 Jun 2025). The RL objective provides better stability and higher final probe accuracies, especially in data-scarce or label-sparse scenarios.

3.3 Generalization and Application Domains

Empirical analyses document that certain pre-training objectives confer better generalization:

• Task-agnostic (image/video) objectives yield superior transfer to out-of-distribution vision RL environments (Kim et al., 10 Jun 2024).
• Task-specific objectives (e.g., behavioral cloning, Q-learning) maximize in-distribution performance but fail under large distribution shifts.
• Pre-training with language (rather than vision) modalities bestows context-processing capabilities essential to compositional RL and sequential decision-making, and enhances downstream learning even in context-absent settings (Takagi, 2022).

4. Transferability, Behavior Induction, and Prompt Engineering

RPT enables models to internalize transferable skills and reasoning styles:

• Behavioral priors pre-trained by modeling action distributions can be rapidly adapted or fine-tuned to new tasks, outperforming model-free RL and imitation learning in sample efficiency and success rate (Singh et al., 2020).
• Prior prompt engineering (pPE) during reward-based pre-training can steer models to adopt distinct reasoning or planning behaviors, with persistent performance gains over inference-time prompting and clear behavioral signatures that survive post-training (Taveekitworachai et al., 20 May 2025).

In science/knowledge mining, self-supervised multi-modal RPT (e.g., hierarchical Transformers for documents, GNNs for community graphs) supports multi-task retrieval, classification, and recommendation tasks with a single, transferable model (Qiao et al., 2021).

5. Critical Limitations and Open Problems

While RPT offers compelling benefits, certain limitations and challenges persist:

• Domain alignment: Benefits are maximized when pre-training data and downstream environment are matched (in-distribution); substantial distribution mismatch (e.g., ImageNet pre-training for locomotion) can hurt performance (Kadavath et al., 2021).
• Reward specification: Verifiable, automatic rewards are crucial for stability and scale; reliance on learned or heuristic rewards can introduce “reward hacking” or misalignment, though RPT’s framework can mitigate this if the reward can be derived intrinsically or from ground-truth (Dong et al., 9 Jun 2025, Ghosh et al., 13 Jun 2025).
• Scaling and Integration: Unified experimental frameworks and benchmarks are necessary to meaningfully compare RPT implementations across domains (Xie et al., 2022, Kim et al., 10 Jun 2024).
• Real-world transfer: Despite strong results in simulation or on curated benchmarks, transfer to complex, high-DoF, real-world robotics and safety-critical domains still faces open scalability and generalization challenges (Yang et al., 2023, Zhang et al., 2023).

6. Implications for Scaling and Future Research

RPT is positioned as a general scaling paradigm for both LLM and RL system pre-training:

• Provides a continuum between pre-training and fine-tuning, minimizing objective gaps, and supporting emergent behaviors such as reflection and self-correction before any reward-driven optimization (AI et al., 5 Apr 2025).
• Powers general-purpose, sample-efficient adaptation in robotics, vision, speech, and scientific knowledge mining.
• Invites new questions on architectural innovation, curriculum design, intrinsic reward engineering, and parameter-efficient transfer across modalities and environments.

Future research directions include:

• Integrating non-contrastive/generative rewards in RL-based vision pre-training (Ghosh et al., 13 Jun 2025)
• Developing adaptive and modular pre-training pipelines that can blend task-agnostic and task-specific objectives for robust generalization (Kim et al., 10 Jun 2024)
• Scaling RPT regimes for lifelong/continual learning and multi-agent systems (Xie et al., 2022)
• Further exploration of prompt engineering and behavior scaffolding as axes for controlling and specializing LLM behaviors (Taveekitworachai et al., 20 May 2025)

7. Representative Summary Table

RPT Domain	Core Objective	Generalization
Language (LLM)	RL-based next-token reasoning (verifiable reward)	Strong, scalable
Vision	Value bootstrapping via image transformations	Strong OOD, robust
Robotics/Sensorimotor	Masked, predictive, sequence modeling of trajectories	Cross-task, cross-robot
Knowledge mining	Multi-modal, self-supervised contrastive objectives	Multi-task transfer
Prompt/Behavioral	Reward-guided reasoning/planning prompt templates	Style control, efficient

References to Key Formulas and Results

• Language RPT reward: $r_t = 1$ if prediction matches ground-truth, else 0 (Dong et al., 9 Jun 2025)
• RL value function in RL-based vision pre-training: $$Q^*(x, a, \ell) = \mathbb{E}_{x'}[r(x', \ell) + \gamma \max_{a'} Q^*(x', a', \ell)]$$ (Ghosh et al., 13 Jun 2025)
• Empirical scaling law: $P(C) = \frac{A}{C^{\alpha}} + P^*$ (Dong et al., 9 Jun 2025)
• Behavioral prior likelihood: $$\log p(a|s) = \log p_z(f_\phi^{-1}(a; s)) + \log |\det \partial f_\phi^{-1}(a; s) / \partial a|$$ (Singh et al., 2020)

RPT is thus recognized as a unifying, scalable paradigm enabling general-purpose, sample-efficient, and robust learning in both foundation models and embodied agents, with actively expanding frontiers in methodology, application, and theory.