Show HN: Reward Is Not Reinforcement Until Admitted
The article discusses an experiment designed to test the thesis that reward is not reinforcement until it is admitted. It outlines a ranking-only setup that evaluates synthetic coding tasks using various selectors to determine the best patch outcomes. The results and metrics from the experiment are documented in multiple reports and JSON files for further analysis.
- ▪The experiment uses a ranking-only setup rather than model fine-tuning.
- ▪It compares different selectors based on their ability to choose the best patch outcomes.
- ▪Results are documented in various reports and JSON files for detailed analysis.
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Story provenance
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Record
| Original publisher | GitHub |
| Canonical URL | https://github.com/nikitph/rewarder |
| Publication time | Mon, 25 May 2026 11:58:49 +0000 |
| Retrieval time | 2026-05-25T12:07:36.708Z |
| Last seen | 2026-05-25T12:07:36.708Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | DO-QiQ5WzTzb |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Governed Reward Experiment Minimal runnable experiment for the thesis: Reward is not reinforcement until admitted. The experiment uses a ranking-only setup rather than model fine-tuning. Each synthetic coding task receives several candidate patch outcomes. A raw selector chooses the patch with the highest raw reward, while a governed selector chooses the patch with the highest admitted reward after invariant, exploit, causal, hidden-test, and delayed-regression checks. Run python3 governed_reward_experiment.py Optional parameters: python3 governed_reward_experiment.py --tasks 100 --candidates 7 --seed 11 Run the multi-seed selector and ablation suite: python3 governed_reward_experiment.py \ --suite \ --tasks 100 \ --seed-start 10 \ --seed-end 30 \ --candidate-grid 3,5,7,10 \ --out…
Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.