Online Reward Poisoning in Reinforcement Learning With Convergence Guarantee
Bibliographic record
Abstract
This paper studies the online reward poisoning problem, wherein an adversary deliberately manipulates the reward function during training to mislead the learning agent into adopting a mischievous policy. While the majority of existing reward poisoning research focuses on offline attacks, which assume prior knowledge of the transition probability, our work explores a more practical yet challenging dynamics-agnostic scenario. Specifically, we consider the scenario where the adversary has access to the agent’s replay buffer and can modify the reward data without the knowledge of transition probabilities. We formalize the poisoning task as an optimization problem and employ a reformulation method to circumvent the double-sampling issue. The proposed algorithm is provably convergent in the tabular setting and can be extended to the function approximation setting, where the poisoned reward network and the poisoned Q-value network are jointly learned to solve the problem. The algorithm’s effectiveness is validated through four distinct experimental evaluations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".