RLBS: An Adaptive Backtracking Strategy Based on Reinforcement Learning for Combinatorial Optimization
Bibliographic record
Abstract
Combinatorial optimization problems are often very difficult to solve and the choice of a search strategy has a tremendous influence over the solver's performance. A search strategy is said to be adaptive when it dynamically adapts to the structure of the problem instance and identifies the areas of the search space that contain good solutions. We introduce an algorithm (RLBS) that learns to efficiently backtrack when searching non-binary trees. Branching can be carried on using any usual variable/value selection strategy. However, when backtracking is needed, the selection of the node to target involves reinforcement learning. As the trees are non-binary, we have the opportunity to backtrack many times to each node during the search, which allows learning which nodes generally lead to the best rewards (that is, to the most interesting leaves). RLBS is evaluated for a scheduling problem using real industrial data. It outperforms classic (nonadaptive) backtracking strategies (DFS, LDS) as well as an adaptive branching strategy (IBS).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".