Raw LLM Responses
Inspect the exact model output for any coded comment.
Look up by comment ID
Random samples — click to inspect
G
The BIG QUESTION IS WHY A LOT OF PEOPLE LONELY AND ALONE?????
And this caused th…
ytc_UgzSXi9Ku…
G
Forget that, AI is gonna cause a plummet in births. Families are expensive. If t…
ytc_Ugw93Ss35…
G
The day Ai can cry over the beauty of a sunset , have butterflies when it sees t…
ytc_Ugyr2D35e…
G
Not AI centers, but prisons for those who know the truth about our realm and God…
ytc_Ugx-h3VKT…
G
One thing that ai bros always tell me is “you shouldn’t waste your life learning…
ytc_UgzDmxuz3…
G
AI investing is definitely fascinating. The idea is that algorithms can process …
ytc_UgxkrCFN3…
G
How about your magic 8ball, did that give you any information? How about your ne…
rdc_ogt0jyf
G
they dont want it talking about religion and life because it will expand our min…
ytc_Ugzt3TLzl…
Comment
Typically, gamma is viewed as part of the problem, not of the algorithm. A reinforcement learning algorithm tries for each state to optimise the cumulative discounted reward:
r1 + gamma*r2 + gamma^2*r3 + gamma^3*r4 ...
where rn is the reward received at time step n from the current state. So, for one choice of gamma the algorithm may optimise one thing, and for another choice it will optimise something else.
However, when you have defined a certain high-level goal, there still often remains a modelling choice, as many different gamma's might satisfy the requirements of the goal.
In general, most algorithms learn faster when they don't have to look too far into the future. So, it sometimes helps the performance to set gamma relatively low. A general rule of thumb might be: determine the lowest gamma min_gamma that still satisfies your high-level goal, and then set the gamma to gamma = (min_gamma + 1)/2.
Hope that solves your query.
youtube
AI Governance
2020-10-20T11:4…
Coding Result
| Dimension | Value |
|---|---|
| Responsibility | none |
| Reasoning | unclear |
| Policy | none |
| Emotion | indifference |
| Coded at | 2026-04-27T06:26:44.938723 |
Raw LLM Response
[
{"id":"ytr_Ugy6Z95H4v2LC9IASRV4AaABAg.9EYLk423JyI9Ep75vu312f","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"indifference"},
{"id":"ytr_UgyJMpeIHjcqnIf6Y8h4AaABAg.9E8GhxSDC279F1boaf47QE","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"indifference"},
{"id":"ytr_UgwPbs2bnV77PjCfhVd4AaABAg.9CTXRS-k8hy9CoQzbBKFcV","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"indifference"},
{"id":"ytr_UgxG7kzon6DLRVupZgp4AaABAg.9BZRM0p718N9C5HvQ4GT3d","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"indifference"},
{"id":"ytr_UgzHllJRZoEZOWi2lpF4AaABAg.9Ai2NdWCd4w9C5HgCvJBZE","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"indifference"},
{"id":"ytr_UgzgODDBLXrwJ2fZ0hx4AaABAg.99-PLRlb7X59C5IFdJOXTm","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"indifference"},
{"id":"ytr_Ugx6Kj_6y2_xnNW2dK94AaABAg.97TxpHm0YzE97hYkPCE3mm","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"indifference"},
{"id":"ytr_UgxEmLvHkBgAbOV_D7l4AaABAg.90THB0z1tbM92U4VgjA9ds","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"approval"},
{"id":"ytr_UgxnIjAVfQcdYHp72694AaABAg.9-nQirXxply93-nOhZOEa2","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"indifference"},
{"id":"ytr_UgzymuNIIrFmE0ki5D54AaABAg.8zbUzxAfKXO8zwbXpqvP3W","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"indifference"}
]