8 New Applications of Test-Time Scaling 
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8 New Applications of Test-Time Scaling 
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We've noticed a huge interest in test-time scaling (TTS), so we decided to explore this concept further. Test-time compute (TTC) refers to the amount of computational power used by an AI model when generating a response. Many researchers are now focused on scaling TTC, as it enables slow, deep "thinking" and step-by-step reasoning, which improves overall models' performance.
text
We've noticed a huge interest in test-time scaling (TTS), so we decided to explore this concept further. Test-time compute (TTC) refers to the amount of computational power used by an AI model when generating a response. Many researchers are now focused on scaling TTC, as it enables slow, deep "thinking" and step-by-step reasoning, which improves overall models' performance.
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Here are 8  fresh studies on test-time scaling:
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Here are 8  fresh studies on test-time scaling:
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1. 
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1. 
Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth
  Approach (2502.05171)
https://huggingface.co/papers/2502.05171
2502.05171
paper
resource
https://huggingface.co/papers/2502.05171
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Introduces an LM that scales TTC by reasoning in latent space instead of generating more tokens with no special training.  Here, a recurrent block to processes information iteratively.
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Introduces an LM that scales TTC by reasoning in latent space instead of generating more tokens with no special training.  Here, a recurrent block to processes information iteratively.
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2. 
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2. 
Generating Symbolic World Models via Test-time Scaling of Large Language
  Models (2502.04728)
https://huggingface.co/papers/2502.04728
2502.04728
paper
resource
https://huggingface.co/papers/2502.04728
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Shows how TTS is applied to enhance model's Planning Domain Definition Language (PDDL) reasoning capabilities, which can be used to generate a symbolic world model.
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Shows how TTS is applied to enhance model's Planning Domain Definition Language (PDDL) reasoning capabilities, which can be used to generate a symbolic world model.
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3. 
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3. 
Can 1B LLM Surpass 405B LLM? Rethinking Compute-Optimal Test-Time
  Scaling (2502.06703)
https://huggingface.co/papers/2502.06703
2502.06703
paper
resource
https://huggingface.co/papers/2502.06703
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Analyzes optimal TTS strategies and shows how small models can outperform much larger ones.
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Analyzes optimal TTS strategies and shows how small models can outperform much larger ones.
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4. 
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4. 
Llasa: Scaling Train-Time and Inference-Time Compute for Llama-based
  Speech Synthesis (2502.04128)
https://huggingface.co/papers/2502.04128
2502.04128
paper
resource
https://huggingface.co/papers/2502.04128
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Shows how TTS improves expressiveness, timbre consistency and accuracy in speech synthesis with Llasa framework. It also dives into benefits of scaling train-time compute.
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Shows how TTS improves expressiveness, timbre consistency and accuracy in speech synthesis with Llasa framework. It also dives into benefits of scaling train-time compute.
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5. 
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5. 
Rethinking Fine-Tuning when Scaling Test-Time Compute: Limiting
  Confidence Improves Mathematical Reasoning (2502.07154)
https://huggingface.co/papers/2502.07154
2502.07154
paper
resource
https://huggingface.co/papers/2502.07154
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Suggests a modified training loss for better reasoning of LLMs when scaling TTC.
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Suggests a modified training loss for better reasoning of LLMs when scaling TTC.
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6. 
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6. 
Adaptive Graph of Thoughts: Test-Time Adaptive Reasoning Unifying Chain,
  Tree, and Graph Structures (2502.05078)
https://huggingface.co/papers/2502.05078
2502.05078
paper
resource
https://huggingface.co/papers/2502.05078
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Unifies the strengths of chain, tree, and graph paradigms into one framework that expands reasoning only on necessary subproblems.
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Unifies the strengths of chain, tree, and graph paradigms into one framework that expands reasoning only on necessary subproblems.
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7. 
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7. 
Sample, Scrutinize and Scale: Effective Inference-Time Search by Scaling
  Verification (2502.01839)
https://huggingface.co/papers/2502.01839
2502.01839
paper
resource
https://huggingface.co/papers/2502.01839
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Explores scaling trends of self-verification and how to improve its capabilities with TTC.
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Explores scaling trends of self-verification and how to improve its capabilities with TTC.
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8. 
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8. 
CodeMonkeys: Scaling Test-Time Compute for Software Engineering (2501.14723)
https://huggingface.co/papers/2501.14723
2501.14723
paper
resource
https://huggingface.co/papers/2501.14723
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Explores how scaling serial compute (iterations) and parallel compute (trajectories), can improve accuracy in real-world software engineering issues.
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Explores how scaling serial compute (iterations) and parallel compute (trajectories), can improve accuracy in real-world software engineering issues.
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Also, explore our article about TTS for more -> 
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Also, explore our article about TTS for more -> 
https://huggingface.co/blog/Kseniase/testtimecompute
https://huggingface.co/blog/Kseniase/testtimecompute
link
8 New Applications of Test-Time Scaling 

We've noticed a huge interest in test-time scaling (TTS), so we decided to explore this concept further. Test-time compute (TTC) refers to the amount of computational power used by an AI model when generating a response. Many researchers are now focused on scaling TTC, as it enables slow, deep "thinking" and step-by-step reasoning, which improves overall models' performance.

Here are 8  fresh studies on test-time scaling:

1. https://huggingface.co/papers/2502.05171
Introduces an LM that scales TTC by reasoning in latent space instead of generating more tokens with no special training.  Here, a recurrent block to processes information iteratively.

2. https://huggingface.co/papers/2502.04728
Shows how TTS is applied to enhance model's Planning Domain Definition Language (PDDL) reasoning capabilities, which can be used to generate a symbolic world model.

3. https://huggingface.co/papers/2502.06703
Analyzes optimal TTS strategies and shows how small models can outperform much larger ones.

4. https://huggingface.co/papers/2502.04128
Shows how TTS improves expressiveness, timbre consistency and accuracy in speech synthesis with Llasa framework. It also dives into benefits of scaling train-time compute.

5. https://huggingface.co/papers/2502.07154
Suggests a modified training loss for better reasoning of LLMs when scaling TTC.

6. https://huggingface.co/papers/2502.05078
Unifies the strengths of chain, tree, and graph paradigms into one framework that expands reasoning only on necessary subproblems.

7. https://huggingface.co/papers/2502.01839
Explores scaling trends of self-verification and how to improve its capabilities with TTC.

8. https://huggingface.co/papers/2501.14723
Explores how scaling serial compute (iterations) and parallel compute (trajectories), can improve accuracy in real-world software engineering issues.

Also, explore our article about TTS for more -> https://huggingface.co/blog/Kseniase/testtimecompute
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