# Excalidraw Data
## Text Elements
Quiet-STaR: Language Models Can Teach Themselves to
Think Before Speaking ^kBlyLraV
IDEA FROM? ^9VMoRVD8
STaR: Self-Taught Reasoner
LMs can improve reasoning ability on QA datasets by sampling rationales to attempt to answer questions ^CQbV2u2h
IDEA FROM? ^opbMBPEL
Problem? ^uRwJ2Dp3
Only on reasoning task;
Generalization is not Good ^KusOrxKx
Process ^MnA4RcB5
1. LM learn from the diverse tasks from a large internet text corpus
2. Leverage pre-existing reasoning ability to generate rationales and train the LM on them with a REINFORCE-based reward ^2zrm4z7y
Example ^hZU2X3Ws
Contributions ^UZoCndsJ
Contributions:
1. generalize STaR to learn reasoning from diverse unstructured text data
2. parallel sampling algorithm
3. introduce custom meta-tokens at the start and end of each thought to follow the LM to learn that it should be generating a rationale and when it should make a prediction based on that rationale.
4. mixing head
5. non-myopic loss
6. performs better ^ak8RB5ky
Experiments Results on GSM8K and Commonsense QA ^hcYbtT8R
algorithm ^Za6egSRx
Three Main Steps ^3nda6CAa
1. Parallel rationale generation: generating r rationales of length t;
insert learned <startofthought> and <endofthought> tokens to mark each rationale's start and end
2. Mixing post-rationale and base predictions; train a mixing-head - a shallow MLP producing a weight determining how much the post-rationale next-token predicted logits should be incorporated compared to the base language model predicted logits
3. Optimizing rationale generation; optimizing the rationale generation parameters(start/end tokens and LM weights) to increase the likelihood of rationales that makes future text more probable. ^7EIS8qVy
Think ^rsf76pvF
Talk ^eZirZJjd
Learn ^izzXY0Jy
Parallel Generation ^SxhOE8Ni
three-layer MLP with ReLU activation ^GSnFAD0v
Mixing Head ^Khc3MytL
Forward Pass and Teacher Forcing ^xInF7gyf
Optimize Meta Token ^HeyIssV7
Optimizing the representation of these tokens, especially the <|startofthought|> token, is crucial but challenging due to the discrete nature of the rationale tokens. We initialize the start and end token embeddings to the embedding corresponding to the em dash, ”−−−”, which often appears in text data to denote a pause or thought. ^ceTRS2EW
Zero-shot performance on Quiet-STaR applied to chain-of-thought on GSM8K ^AHiYWfVn
Limitations:
1. Do not support dynamically predicting when to generate or end, a rationale
2. Only works on 7B model ^QPNFZ9Dj
Quiet-STaR: Language Models Can Teach Themselves to
Think Before Speaking ^v4ZdxArB
Let's verify step by step ---OPENAI
^uD7dsxyG
Outcome Supervision / Process Supervision ^DaTq6qSi
Process Supervision outperforms outcomes supervision for training models to solve problems from the challenging MATH dataset. ^NrlQ0STR
PRM800K dataset, the complete dataset of 800,000 step-level human feedback labels used to train our best reward model. ^BKcogJcT
Why Process Supervision? ^rmLqSUan
1. Hallucination. CoT can help the model to reason but it will cause hallucination
2. Outcome-supervised reward models only use the final results of the model's chain-of thought; they regularly use incorrect reasoning to reach the correct final answer
3. Process Supervision is much easier for humans to interpret and provides more precise feedback, since it specifies the exact location of any errors that occur ^BI7uGgh0
Data Collection Example ^iKFkSz1J
Math Dataset ^l7rJS8IQ
ORMs ^bHXOT5aY
1. train the ORM to predict whether each solution is correct or incorrect; usually determine correctness by automatically checking the final answer
2. automatic grading used to determine ORM targets is not perfectly reliable ^ScDjQmaE
1. train PRMs to predict the correctness of each step after the last token in each step
2. define the PRM score for a solution to be the probability that every step is correct under the PRM ^ZyqQds8V
PRMs ^vV9SaMg8
Visualize ^U44qN4zT
Result
Comparison ^bJYR8og5
EXPERIMENT ^sUHbnn86
Large Scale ^ynatAaW3
Finetune all models from GPT-4; Train the most reliable ORM and PRM ^WvhHk7SQ
Small scale ^MJVbU2YS
direct c ^3d8aYzYI
Generator to generate solutions in a newline delimited step-by-step format ^FHL3sY28
Explanation ^3bwc712i
Data Collection ^3c6Sfn6a
Specifically, we few-shot generate solutions to MATH training problems, filter to those that reach the correct final answer, and finetune the base model on this dataset for a single epoch. This step is not intended to teach the generator new skills; it is intended only to teach the generator to produce solutions in the desired format. ^CBIYRo7T
To collect process supervision data, we present human data-labelers with stepby-step solutions to MATH problems sampled by the large-scale generator. Their task is to assign each step in the solution a label of positive, negative, or neutral,
A positive label indicates that the step is correct and reasonable. A negative label indicates that the step is either incorrect or unreasonable. A neutral label indicates ambiguity. In practice, a step may be labelled neutral if it is subtly misleading, or if it is a poor suggestion that is technically still valid. ^kKyzNJ0z
Explanation ^lREgyqX6
PRMS / ORMs ^Fw6Y9kZ6
PRM800K ^hFAX1LrA
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## Drawing
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```
%%