Evaluation: Preference Gains, Probability Leakage and Format Failures

Task and Prerequisites: Evaluate Beyond the Training Objective

The final chapter turns the previous mathematics and engineering into a boundary-bound experimental conclusion. Readers need to be able to calculate logprob, KL, limited expectations, and understand response-only and fixed references. We keep both limited strategies and complete decoder without lending it a precise amount of money from the former. The project version is run from fixed Seed to budget, testing does not involve hyperparametric selection, and the format probe is used only for post-freezing diagnostics.

A limited reference to the test to achieve a lab.py is the four pre-set values context. It has only four possible actions, so that the correct quality, short quality, oracle expectations and forward KL are accurate enough to be reconciled without Monte Carlo. The serial version of the text_lab.py may produce any byte path, with only one of the four specified answers and does not expect the candidate conditions to be fully strategic. The mission is semantic, but with different probabilities, charts and fields have to be separated to avoid turning the mathematical convenience of a small model into a statement of capability that another model is not running.

Example 1: What Behavioral Improvement Means for a Finite Policy

Fixed Seed 13, SFT and DPO 200 each, with limited strategy correct quality on test ranging from about 0.50911 to about SFT. 894645 Then go to the DPO about. 984084; short mass is about 0. 515398 、 0.660011 、 0.980910. The oracle is expected to be about 0%. 412491 、 0.826647 、 0.980266. The last number can be restored independently with 0.9844084 minus 0.2 times one minus 0.9801010; the margin is derived from the full mass distribution of the four candidates instead of picking a good output.

DPOs are about 0 416272 relative to the accuracy of SFT references. The reference is zero for their own KL; the base from KL from SFT is about 2. 026211, which is a differential distribution of random starting points below fixed SFT benchmarks, not an optimal penalty record in training. The BT small incentive model is sorted in order to leave Pair fully intact, indicating that its two characteristics are sufficient to express the synthesis comparison; it does not learn human judgment or give decoder external rewards. All these values must be interpreted with four action spaces and known rules.

The independent inspection is based on a four-by-four probability of re-establishing three expectations and an average of four context levels; the independent examination is based on a one-to-one, oracle-altitude and KL-non-negative in numerical tolerance. If you use a greedy action instead of a four-probability exercise, another statistic is obtained that may conceal the quality of the errors that still exist. Both the full parameter center differentials and the real training of the limited model have been adopted, which can only indicate that the definition algorithmal links are correct in the sample, without extrapolating all initializations from one Seed.

Example 2: Conflicting Objectives in a Complete Decoder

The complete sequence model 11472 parameters, CPU single-line, SFT 60 steps to freeze reference, and DPO 40 steps. Training DPOs dropped from log2 to about 0.693147 before final update. 020371 The short answer to the canonical four-point shortcuts is four-to-four in both the SFT and the DPO, so the DPO does not produce observable improvements in this small greeny indicator, which has reached its full point. Only the four correct samples of the DPOs are shown to hide the ceiling.

At the same time, the average complete answer for the test model has deteriorated from about 1.284213 to 32.205868; on average, the answer has changed from 0.1328550 to 3.331642. The reason is not that the denominators are missing, but that one third of the models are still correct and lengthy, and the preference phase is significantly lower as Loser. This test is consistent with chapter II ' s objective conflict: the frequency of mimicry demonstrations is not the same task as the bias. It does not automatically equate to “DPOIt's broken.”, nor can it be changed to all performance improvements.

A stronger diagnosis is the absolute probability of Chapter VII: x=-3.5, the correct short answer for NEG fell from about 0.492814 to 0.103779, although the probability of a candidate's condition is similar. This suggests that even if the real path probability of a predetermined Winner is reduced, and the probability moves to an unlisted string, the DPO can still be better than the target. The absolute logprob, total quality and probabilities of the candidate should be kept at the same time, and a beautiful column of the candidate softmax cannot be considered as a complete generation distribution.

Pitfalls When Evaluation Compares Models

Implicit_pair_accuracy in the sequenced receipt. $\beta$ Multiply whether the relative difference between reference logs is positive. All margins are exactly zero when SFT is equal to reference, at a strict rate greater than zero; this does not mean that SFT is completely wrong about preferences, but rather that it has no preference for itself. This indicator specifically diagnoses the hidden DPO margin, which should be distinguished from the original response logprob ranking, actual generation and oracle. Saving the name to “Accuracy of responses” would cause serious reading errors.

The test averages about 0.156170, which does not contain a flow of quality beyond the candidate, so it may be very small and the whole creation behaviour has changed markedly. The project does not report the undetected full-language distribution of KL tops, nor does it include the DPO. $\beta$ Directly as an actual KL threshold. To estimate the full path KL, a limited tree or sampling strategy, including EOS and abort, with error and support conditions, is required; this additional estimate is not implemented at this time.

Format Transfer: Preserve Every Failure

After fixed weights, probe_formats.py runs five planned changes once: Unicode minus, X field becomes value, English negative two, plain sign +, and positive value. They all keep the corresponding values real, but exceed the “x=point decimal; anwer:” caanonic format. All results are written in the same document, without filtering them for success or adjusting their parameters. The format migration diagnosis was separated from the four caononic tests and no five failed to modify the original fixed test denominator.

In real output, Unicode subtraction hints " x=−2.0; answer: " Got “G” and EOS; exhibited positive sign " x=+2.0; answer: " Got “NEG”, symbol error. Negative value value fields produce a byte string with the positive value value value field that contains illegal UTF-8 replacement characters and do not end within the 24-step cap; English negative two gets format outer segments that do not end. Five strict correct values are zero. The original token number, valid_utf8, EOS and the complete display text are saved, replacing it with a rating after it has not been modified as the original byte.

The result suggests that the model may have learned the negative ASCII and fixed position patterns in the caonical format, rather than the numerical synonym of the format. The fixed bytes make the Unicode decoding possible, without the model having learned to equate its three bytes with ASCII. The learned position with very few hints also causes a distributional shift in the length of the field. It cannot be asserted from this failure that all models are necessarily so; It is possible to say precisely that the current freeze micromodels failed in these five probes and, as a result, countered the extrapolation of “canonical full-scale judgement”.

The Beta Grid and Selection Boundaries

Reservation with limited reference $\beta$ 0.1 、 0.5, and two or three validation points, the other budget is the same. 398593 、 0.410743 、 0.334603, is not single; the test expects oracle about 0%. 999108 、 0.999653 、 0.995182. The change affects the margin scale, gradient and saturation, so the limited step results do not comply with " $\beta$ A simple slogan for a larger, smaller KL. This watch explains the difference between training dynamics and theory, not the three points of general excellence. $\beta$ 。

Sequence Version $\beta$ 0.1 and 60 plus 40 updates are prefixed without formatting probes or test selection. If you see failure in the future to expand formatting data, that is a reasonable new experiment, but require new versions, reclassification validations and new setting conditions; it is not possible to overwrite modified results and fail to claim that the same frozen version remains robust. The comparison model improvement should match the training budget, data and decoding rules, retain the previous failure type, and avoid adding only new test templates that the model has just learned.

Uncertainty and Untested Behavior

The four values test are too small and design-related to give a narrow confidence range to the natural language as a whole. Zero failure or full score is only these observations, not an assurance of unknown distribution. We have no more Seed sequence training, no random sampling success estimates, no artificial preference for duplicates, and no open response task. No ranking of rewards can be changed to a secure, factual or commercially available certificate.

The project did not achieve PPO, online reward feedback, LoRA or multiple rounds of dialogue; DPO uses offline comparisons and full parameter gradients. The sequence checkpoint only freezes evaluations and does not contain the full AdamW recovery state. SSD receipts from the standard library can be accurately renewed by one step that remains independent and cannot be replaced with an unsavory SSD training status. Not by reducing the complete decoder training that has been done, but by ensuring that each action corresponds to the true code and evidence.

The Final Validation Package

The project compression package should contain limited reference, copies of decoder, division_lab, strict format ratings, formatting probes, two fixed status and receipt sets, and 17 tests. Run the independent decompression directory first and then load the limited indicators separately into a separate level-state.pt rating and revaluation. The source code and data contract summaries are bound and the author ' s receipts do not annex the independent acceptance document that was subsequently generated by the main task. The course page code indicates standalone, project orcommand and prevents the use of segments that require model files as a single document standard library example.

The ability to close this course is to model and compare objectives, to deduce BT/KL/DPO, to train a truly full parameter decoder, and to identify successes and failures with independent evidence. It does not call online API training or replace the complete model with a checklist. All formal conclusions are mission, data, models and budget boundaries; readers should be able to recalculate each indicator from the same frozen product, preserving the conflict and failure, rather than merely remembering “It's better when you're aligned.”, a slogan that has no operational meaning.

The style reads only diagnostics to prevent the deterioration of the mixed NLL to all kinds of capacity: long answers are preferred to active low-pressure categories, but short answers average NL from 0.690142 to 2. 658300, providing independent evidence. BOS shielding is only a greedy decoding and is not part of the rreponse_logps original softmax; thus four are behavioral observations subject to the decoding rule.

The data rules and formatting probes are made public with the project, and readers can recalculate without trusting the author’s subjective ratings. Independent acceptances should first check the freezing model against the source identity and then check the original number according to the same stop code rule; if the rating rule is changed, they must be registered as a new evaluation agreement rather than covering old results.

import math
correct=.9840835773986907;brief=.9809099070167226
oracle=correct-.2*(1-brief)
assert abs(oracle-.9802655588020353)<1e-12
sft_nll=1.2842129468917847;dpo_nll=32.205867767333984
assert dpo_nll>sft_nll
winner_abs=.1037786202280831;candidate_mass=.1037786202280831
conditional=winner_abs/candidate_mass
assert conditional==1 and winner_abs<.11
failures=[True,True,True,True,True]
print('finite expected oracle',oracle,'sequence NLL change',dpo_nll-sft_nll,'winner conditioned',conditional,'format failures',sum(failures))