A new Q(lambda) with interim forward view and Monte Carlo equivalence
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Bibliographic record
Abstract
Q-learning, the most popular of reinforcement learning algorithms, has always included an extension to eligibility traces to enable more rapid learning and improved asymptotic performance on non-Markov problems. The λ parameter smoothly shifts on-policy algorithms such as TD(λ) and Sarsa(λ) from a pure bootstrapping form (λ=0) to a pure Monte Carlo form (λ=1). In off-policy algorithms, including Q(λ), GQ(λ), and off-policy LSTD(λ), the λ parameter is intended to play the same role, but does not; on every exploratory action these algorithms bootstrap regardless of the value of λ, and as a result they fail to approximate Monte Carlo learning when λ = 1. It may seem that this is inevitable for any online off-policy algorithm; if updates are made on each step on which the target policy is followed, then how could just the right updates be 'un-made' upon deviation from the target policy? In this paper, we introduce a new version of Q(λ) that does exactly that, without significantly increased algorithmic complexity. En route to our new Q(λ), we introduce a new derivation technique based on the forward-view/backward-view analysis familiar from TD(λ) but extended to apply at every time step rather than only at the end of episodes. We apply this technique to derive first a new off-policy version of TD(λ), called PTD(λ), and then our new Q(λ), called PQ(λ).
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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