PPRS
A reinforcement learning method based on potential-based reward shaping that uses a latent world model to automatically compute task progress potential and convert progress metrics into dense per-step rewards. It alleviates the credit assignment problem under sparse rewards while preserving optimal policy invariance.
Timeline (last 90 days)
The innovation of PPRS lies in defining the potential Φ directly as a progress metric in the latent world specification, rather than a heuristically designed intermediate reward, thereby preserving the theoretical losslessness of potential-based shaping.
Tev simultaneously provides the data infrastructure (Tev-Gym), the training method (PPRS), and the validation results (Tev-27B), forming a complete closed loop.
PPRS (Progress Potential Reward Shaping) computes the reward for each step based on the potential increment of a potential function Φ, and is a concrete instance of the potential-based shaping framework proved by Ng et al. in 1999, satisfying reward shaping invariance.
If progress metrics are poorly designed (for example, counting only the number of API calls), agents may find shortcuts through reward hacking, such as marking compliance as complete without actually checking the rules.
Tev-style PPRS is suited to scenarios where tasks have clear intermediate milestones, where open-source models are needed to handle multi-hour workflows, where resources can be invested in building a latent world model, and where the budget allows task-aware checkpointing.
Tev-style PPRS should be avoided for tasks that are truly unstructured with no natural progress metric, for real-time execution requiring sub-second latency, for environments that change faster than the latent world specification can be updated, or where a closed-source model already handles long-horizon tasks.
All Facts (6)
The innovation of PPRS lies in defining the potential Φ directly as a progress metric in the latent world specification, rather than a heuristically designed intermediate reward, thereby preserving the theoretical losslessness of potential-based shaping.
50%UnverifiedTev simultaneously provides the data infrastructure (Tev-Gym), the training method (PPRS), and the validation results (Tev-27B), forming a complete closed loop.
50%UnverifiedPPRS (Progress Potential Reward Shaping) computes the reward for each step based on the potential increment of a potential function Φ, and is a concrete instance of the potential-based shaping framework proved by Ng et al. in 1999, satisfying reward shaping invariance.
50%UnverifiedIf progress metrics are poorly designed (for example, counting only the number of API calls), agents may find shortcuts through reward hacking, such as marking compliance as complete without actually checking the rules.
50%UnverifiedTev-style PPRS is suited to scenarios where tasks have clear intermediate milestones, where open-source models are needed to handle multi-hour workflows, where resources can be invested in building a latent world model, and where the budget allows task-aware checkpointing.
50%UnverifiedTev-style PPRS should be avoided for tasks that are truly unstructured with no natural progress metric, for real-time execution requiring sub-second latency, for environments that change faster than the latent world specification can be updated, or where a closed-source model already handles long-horizon tasks.
50%