Multi-timescale nexting in a reinforcement learning robot
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The term ‘nexting’ has been used by psychologists to refer to the propensity of people and many other animals to continually predict what will happen next in an immediate, local, and personal sense. The ability to ‘next’ constitutes a basic kind of awareness and knowledge of one’s environment. In this paper we present results with a robot that learns to next in real time, making thousands of predictions about sensory input signals at timescales from 0.1 to 8 seconds. Our predictions are formulated as a generalization of the value functions commonly used in reinforcement learning, where now an arbitrary function of the sensory input signals is used as a pseudo reward, and the discount rate determines the timescale. We show that six thousand predictions, each computed as a function of six thousand features of the state, can be learned and updated online ten times per second on a laptop computer, using the standard temporal-difference( λ) algorithm with linear function approximation. This approach is sufficiently computationally efficient to be used for real-time learning on the robot and sufficiently data efficient to achieve substantial accuracy within 30 minutes. Moreover, a single tile-coded feature representation suffices to accurately predict many different signals over a significant range of timescales. We also extend nexting beyond simple timescales by letting the discount rate be a function of the state and show that nexting predictions of this more general form can also be learned with substantial accuracy. General nexting provides a simple yet powerful mechanism for a robot to acquire predictive knowledge of the dynamics of its environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 it