The Online Loop-free Stochastic Shortest-Path Problem.
Why this work is in the frame
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Bibliographic record
Abstract
We consider a stochastic extension of the loop-free shortest path problem with adversarial rewards. In this episodicMarkov decision problem an agent traverses through an acyclic graph with random transitions: at each step of an episode the agent chooses an action, receives some reward, and arrives at a random next state, where the reward and the distribution of the next state depend on the actual state and the chosen action. We consider the bandit situation when only the reward of the just visited state-action pair is revealed to the agent. For this problem we develop algorithms that perform asymptotically as well as the best stationary policy in hindsight. Assuming that all states are reachable with probability a>0 under all policies, we give an algorithm and prove that its regret is O(L^2 sqrt(T|A|)/a), where T is the number of episodes, A denotes the (finite) set of actions, and L is the length of the longest path in the graph. Variants of the algorithm are given that improve the dependence on the transition probabilities under specific conditions. The results are also extended to variations of the problem, including the case when the agent competes with time varying policies.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.008 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it