Read the equation
The next parameter state is produced by an update operator using the current parameters, training batch, and reward signal. This makes explicit the mechanism by which training can persistently change a model. A prompt-driven output change does not imply this parameter-update operation occurred.
Terms & definitions
| Term | Meaning in this source |
|---|---|
| theta_k | Trainable parameters at step k |
| U | Training update operator |
| training_batch | Data used for the update |
| reward | Optimization feedback |
How it works
Read the definition from its right-hand expression to the quantity on the left. Its surrounding source records the intended input, state, or parameter context. Where the source leaves an operation unspecified, this page preserves that gap.
Assumptions & limits
Source recovery establishes that the expression appears in this record. Its mathematical validity, implementation behavior, and interpretation must be assessed separately. See the linked source and research notes for the checks available for this version.
Original context
Read the surrounding source passage
Yi∼Pθ ⋅∣Pi,Ui,Ci, where Pi is the system prompt, Ui the request, and Ci the conversation context. For genuine learning, a separate update is needed: θk+1=Uθk,training batchk,rewardk. Neither equation assumes the presence or absence of consciousness. They specify what is being changed. Evidence about one should not be presented as evidence about the other without a connecting experiment. 4.3 A reproducibility trap: response caching The notebook keys a cache by the system prompt, conversation, and model identifier. A completed identical request can be answered from that cache. Repeating an identical call in the same kernel is therefore not automatically an independent stochastic trial. Concurrent calls can also race before a cache entry is populated, so the precise behavior depends on invocation order. [S3]
Exact original notation
{\theta }_{k+1}=U\left({\theta }_{k},{training batch}_{k},{reward}_{k}\right).Sources & provenance
{\theta }_{k+1}=U\left({\theta }_{k},{training batch}_{k},{reward}_{k}\right).
{\theta }_{k+1}=U\left({\theta }_{k},{training batch}_{k},{reward}_{k}\right).