Reproduce from a Clean Directory and Report Failures

Verify That the Downloaded Package Runs Independently

The curriculum should be a complete experiment, not several sections of code that rely on the author’s computer’s implied path. After downloading it, the directory contains decoder.py, rab.py, probe_formats.py, tests.py, README, relying on lists, data contracts, source records and evidence. It does not import brother courses, does not read web chapters of JSON to run, and does not require external models or keys. While the Transformer teaching from this package has been achieved, it has been copied by by bytes to the project and the source is recorded, and students need not find another download package to supplement their dependence.

Environmental requirements for Python and PyTorch 2. 6.0 are at least 3.11; the core project itself does not use NumPy or Pillow and cannot rely on other courses for unnecessary installation. The running environment recorded is Python 3. 12.14, PyTorch2. 6.0, CPU one-line. Different hardware and library versions may change float details or random sequences, and the same environment should be strictly reset; The function recreates the probability of being one, gradient constant, reference freezing and evaluation directions, and saves information about the new environment.

Inputs and Outputs for a Complete Reproduction Run

Runs the stdlib lab.py first, which does not require neurological network dependency, verifying the resolution gradient for three actions, the limited differences and the precise list of group means/leaving a baseline. Runs the tes.py, which contains the authenticator, data division, causal mask, sampling re-setting, path rating, KL gradients, freezing check points, and short-training closings. Finally runs the full 40 plus 100 steps with runrun.py train-out my-run and gets new check points and reports; Reread it, probe_formats test for the same new weight output sequential pressure.

All commands are run from the project root directory -- notput is an explicit experimental directory. The package contains the original results of the freeze quoted in sermons, and new experiments write my-run without covering it. Output check points save the current policy and reference, resulting in JSON saving configuration, data summaries, budgets, phased indicators, gradients and parameter changes. Formatting reports are not part of the training and do not change weights; the same checkpoint can read only evaluations repeatedly to validate “The report figures belong to this weight.”, rather than just a row summary printed during the training.

Example 1: Tracing a Report Claim to the Actual File

The report states that "RL updates all 17 arguments". Read rl_tensor_max_updates first, check each value positive; then check the last tensor_gradient_norms of hestory, check each limited non-zero; and finally recreate the model with Checkpoint, which is 2768 and named_parameters seventeen. The reference to Hashi is also checked for consistency with the loaded reference model, and the reference_unchanged is true. Only the trackable count has no gradient or margin, a statement of intent, and is not sufficient to demonstrate that the actual update occurred.

The report states that "the average probability of success of the test is 0 0000325802." Runs the evaluate load of the same checkpoint, re-routing each of the five questions by one, validating the probability of each and approaching one, giving the exact success path an average mass. If you read it again, there will be no independent re-calculation, and the test is likely to repeat the old error seal. The project freeze tests do re-calculate models and authenticators, while other statistics retain issue-by-question data so that readers can write separate checks.

A Manifest Establishes Connections, Not Truth

evidence/source-manifest.json records student source code, README, protocol, check point, numerical report, format pressure results and eight chapters of SHA-256. Each item has a clear relative path, bytes, and summary. It binds the material to detect subsequent changes in the source code, replacement of indicators, or examination of the point array; but the summary does not justify mathematical reasoning, nor replaces positive, negative, gradient and independent evaluation. The course page wraps up a separate list of ZIPs, both of which are used to track different delivery boundaries.

The list does not include future code-version, math-release or downloadbacks generated by web-maintainers. Otherwise, every independent validation in a time-consuming field will render the old list ineffective or even self-referenced. The original author’s evidence is retained alongside the independently validated evidence, refreezing the corresponding source file when it is discovered to change, instead of tampering with the time or using “Autoupdate” to hide the source.

Example 2: A Minimal Counterexample Refutes a Broad Claim

Hypothetically, it is said that the model has “two steps plus reasoning, because it trains greedy 19/20.” The freezing test two plus two is countered by its general statement that it is correct [4,8,EOS], and actual greed [6,2,EOS]. This counter-offensive is enough to deny the full meaning of “All local input is available.”, but does not estimate the overall accuracy of natural mathematical questions. The project gives a precise probability for the whole five test set, so that failure does not stop at the level of a single case.

Another is that "using a relative group comparison is equivalent to reproducing GRPOs." Viewing the actual losses of run.py, only on-policy sampling, leaving a return baseline with tracks score, and not having a multi-wheel probability ratio for old strategies, PPO cropping or group criteria one by one, the algorithm name is not valid. Explaining the original paper is not equal to a recurrence experiment. The course uses PPO/GRPO formula as an import mechanism, and the project clearly names its own limited algorithms, a distinction that would be more helpful in moving later than giving a front name to microexplosives.

Resource Costs and Stopping Conditions

In the current RAM-style calculation model, you have a K-step action number, a random L-length, and a full path with a K-side L-line path, each of which requires a corresponding forward rating; this item K10, L2, only 100, with long text indexes explosion. The training sampling cost increases by B-by-G-by-L response action, and the model follows the length of the context and hidden width. “A hundred is cheap.” Infer that the true reasoning of the RL is a valid assessment. The limited time comes from a fixed 40-100-step cycle, with each fixed two-step cycle, and does not depend on self-reporting in the answer. “I'm done.” 。

The authenticator handles fixed length lists, small integer numbers and several algorithms, so there are no complex expressions, zeros or relativistic explosions. If you change to a free-calculator, the length, tree nodes, integer numbers and operational white lists must be redefined; if you change to natural language, a separate synonym or form authenticator is also required. Keeping these new issues within a clear boundary is more honest than pretending that the course ' s three-line validation covers all the reasoning tasks and provides a clear interface for subsequent expansion.

Diagnose Failures Instead of Rerunning Until Satisfied

If the lab disassembles does not match the difference, check the probabilities to the score symbol first; if the path probabilities and differences are equal, check the sampling and rating support; if the Hashi variation is consulted, check the DEepcopy, the optimizer parameter pool and the shared load; if the training results float, check the seeds, threads, dropout, steps, data summaries and dependencies. If the code is passed but the test fails, retain the model capability failure and it can no longer be repeated as a software failure.

The project did have a problem with receiving and inspection design: the very short two-step smoke training required all volume changes, which were not established in identical cases. It was the test assertion that changed to check process and limited value; the complete freeze training still required actual 17 volume updates and had been met. This is an example of a test that also needs theoretical review and cannot be used to raise noise or change training targets for green ticks. Assue records make this distinction clear, avoiding subsequent miscalculation of a signal.

Complete Deliverables: Passed, Failed, and Not Run

The software and mathematics checks that have been adopted within the explicit contract: rigorous verification, no intersectional division, limited gradient constant equations, one estimate, cause-and-effect rating, sampling replay, reference freeze, complete network update and independent weight evaluation. What has failed is that the training meets and targets four extrapolation, maintaining the alert format in a stable manner. What is not running is multiple training seeds, natural language mathematics, free length reasoning, GPU, large models, remote API and full PPO/GRPO recurrence. Three types of state must occur simultaneously and cannot be blurred at different levels by a single “All completed”.

The end point of the eight chapters is that the reader can independently rebuild and audit a small experiment of reasoning-enhanced learning: starting with a limited sample space and certifier, to extract score, baseline and KL, to receive a real shared parameter decoding device, to run a fixed budget and to determine its capability boundary. Project failure does not render the curriculum incomplete; hiding failure, lack of dependency or non-clothing leads to incomplete learning. Readers should take away an detectable chain of cause and evidence, and the ability to accurately identify what went wrong when the chain was not established.

python lab.py
python tests.py
python run.py train --output my-run
python run.py evaluate --output my-run --split train
python run.py evaluate --output my-run --split test
python probe_formats.py --checkpoint my-run --output my-run/format-probes.json

import json
from pathlib import Path
from run import load, hash_model, evaluate, ROOT
r=json.loads((ROOT/"evidence/results.json").read_text())
m,q=load(ROOT/"evidence")
assert hash_model(q)==r["reference_hash"]
actual=evaluate(m,q,"test")["mean_exact_success"]
assert abs(actual-r["after"]["test"]["mean_exact_success"])<1e-10
print("recomputed",actual)