SYSE.45:4 - Solution
SYSE.45:4.1 - Fix the target and retained configuration
Name the behavior to change and its receiving result: for example, forming valid arguments, selecting a useful observation or applying stable procedural guidance. Name the base model and parameter or adapter set to update. Separate that change from context, memory, tool, controller and environment changes. When the target is assistance selection, consume SYSE.50’s decision unit, available signals, qualified need evidence and fallback; selecting training does not itself establish which help is needed. For learned effort control, consume SYSE.51’s feasible moves, intermediate decision basis, cost accounting and completion obligations; retain externally enforced hard limits.
Specify the external contributions that remain: execution, current observations, access permissions, retrieval when facts change and independent checking where the claim needs it. Compare complete later configurations through C.38/C.11.CRC and C.11. Include stability of the proposed mapping, expected recurrence, target preparation, training, independent qualification, maintenance and the fallback after a failed trial. A cheaper response that loses a necessary contribution is a different result. The Reference comparison selects an explicit rule for its supplied 100-update conditions and manual work for five; recurrence alone selects no learner.
Keep an unchanged baseline and the means to restore it. Establish the task-family boundary and what finding would make training no longer worthwhile.
SYSE.45:4.2 - Obtain experience with interpretable feedback
Collect observation/action trajectories whose inputs, source/configuration and outcomes can be recovered. Include successful alternatives, informative failures, required restraint and changed-condition cases. Use SYSE.49 when the needed experience must be constructed; qualify its feedback before using it as a training signal.
Separate the agent’s actions from tool observations and the judge’s conclusions. For each selected target, identify what counts as correct and why. A failed trace can teach a corrected action or a failure condition; blindly imitating its actions teaches the failure.
Partition construction, adaptive validation and final evaluation by the dependencies that could leak the answer, such as shared scenarios, source instances or near-duplicate trajectories. Keep the final cases outside training and repeated candidate selection. Generated labels remain provisional until their result meaning is qualified.
SYSE.45:4.3 - Select and execute the learning mechanism
First choose the experience transformation that can supply the intended policy target. The learning algorithm then consumes that target; the names supervised learning, reinforcement learning and distillation do not construct it.
| Available experience and intended change | Construct the signal | Further-use question and return |
|---|---|---|
| A qualified action sequence demonstrates the needed behavior | Pair each retained decision history with the agent action it warrants. Keep tool observations as inputs, and fit only the intended agent outputs. Remove incidental target identifiers or values by varying them in applicable examples. | Does the policy choose the action on a new applicable input and abstain on an unsupported one? An unqualified successful transcript returns to result/trajectory assessment. |
| A recorded decision is wrong or unnecessarily costly | An experience-informed teacher proposes a corrected next action at that history. Qualify it against the task, interface and facts the student will actually have. Train the student on that history/action pair without the teacher’s extra experience. | Does the correction remain warranted without hidden teacher facts? Obtain a needed fact or retain support if it does not. The old next observation follows the old action, not the proposed correction. |
| A search or deliberation procedure finds useful candidates | Preserve selected candidate comparisons, evaluation grounds and backtracking decisions as targets where their signals are available to the student. Distillation can transfer how candidates are generated or compared. | Test unseen alternatives and defeated evaluator premises. Imitating a planner’s trace does not transfer its proof or guarantee. |
| Actual attempts supply outcome or process feedback | Bind feedback to the required result and protected conditions. When a final reward leaves the responsible decision unclear, use a qualified intermediate state or a discriminating action contrast to localize the target. Keep an uncertain credit assignment at that evidential strength. | Test the actual result and the relevant intermediate behavior separately. Reward growth with duplicated effects or lost required access fails. |
| Qualified comparative judgements express a preference | Name whose preference, the task/context, the compared action or answer pair and the grounds for its ordering. Give those pairs to a supported preference-training Method and implementation; return a missing judgement or implementation. | Preferred behavior still needs independent factual, permission and protected-result grounds. A rater’s preference supplies no missing world effect. |
Match the update to that signal. Supervised learning fits qualified actions or corrected continuations; reinforcement learning uses attempted actions and qualified reward; distillation transfers the selected teacher or search behavior. CMP.7 supplies learner construction and the distinction between obtaining data, fit and further use. The selected implementation supplies the exact update algorithm, tokenizer and trainable parameters. Bind them to these data transformations and the later input format. Keep the configuration that actually produced each comparison.
Construct tool knowledge and use separately when that is the gap. Tool-Internalized Reasoning separates learning a tool’s documented semantics, supervised preparation on tool-use trajectories, and optimization of subsequent tool reasoning. For an interval setter, the semantic target includes what operation and unit its arguments denote; a trajectory target then puts the call at the right decision with the right observations. A later reward can distinguish the selected tool and argument choice only at its qualified meaning. The source’s special tool vocabulary, document/token mapping and reward are implementation choices, not prerequisites for every adapter. A tool/argument proxy does not establish the resulting service effect. A plain converter or retained description may supply the needed operation more cheaply.
When reducing repeated procedural guidance, vary only the contribution intended to become unnecessary. Keep execution and current observations available. Test separately a stable mapping presented with all its needed inputs and a decision whose new interface fact was withheld. Their different repairs are worked below. Restore needed support rather than rewarding unsupported fluency.
Bound repeated and episode-local updates. Identify the state or parameters being changed, the qualified signal that permits an update, the experience retained, and the lifetime/reset rule. An episode-local adapter may be discarded at the end; durable shared parameters need a recoverable baseline and regression tests across later tasks. A runtime hidden state or learned memory token is not automatically either shared-parameter learning or an external text record. SYSE.52 prepares the exposed working representation, SYSE.43 maintains separately persisted episodes, and this Method governs selected parameter learning. Specialized backbone or latent-memory-module construction remains with its direct implementation.
Choose update extent and stop from applicable held-out behavior and the available means. Test older useful restraint and correct actions alongside the new target. Repeated self-generated failures are observations of failure; frequency does not turn them into positive labels. Reset, revert or return the unqualified signal when the update destroys earlier required behavior.
SYSE.45:4.4 - Compare the intended later use
Through SYSE.46, compare the trained candidate with the baseline under matched task and retained-support conditions. Read actual result, necessary access/use, restraint, protected effects and total effort separately. Adaptive validation selects candidates; an untouched final comparison supports the bounded reliance claim.
Test delayed or shifted use when the claimed benefit includes persistence or adaptation. Identify intervening model, memory, controller or source changes before attributing a difference to training. A policy that uses fewer tools while missing a newly changed fact fails that receiving use.
Retain, revise or reject the candidate according to the comparison. Return bad feedback to its supplier, unsupported transfer to the training question and runtime routing defects to SYSE.47. A learned predictor or auxiliary target retains its MMP/CMP meaning; correct predictions alone do not establish a useful acting policy.