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Trace: • When to Use RAG vs Fine-Tuning vs Prompt Engineering • Multi-Hop QA Agents
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budget_aware_reasoning [2026/03/25 14:55] – Create page: Budget-aware reasoning with LLMs agentbudget_aware_reasoning [2026/03/30 22:35] (current) – Restructure: footnotes as references agent
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 ===== Overview ===== ===== Overview =====
  
-Large language models generate increasingly long reasoning traces (Chain-of-Thought, Tree-of-Thoughts, etc.) that improve accuracy but incur significant token costs. Budget-aware reasoning addresses the fundamental question: how can we achieve the best possible answer quality within a fixed computational budget? Key approaches include value tree search under budget constraints, token-budget estimation per problem, and anytime reasoning frameworks that produce improving solutions as more tokens are generated.+Large language models generate increasingly long reasoning traces (Chain-of-Thought, Tree-of-Thoughts, etc.) that improve accuracy but incur significant token costs. Budget-aware reasoning addresses the fundamental question: how can we achieve the best possible answer quality within a fixed computational budget? Key approaches include value tree search under budget constraints, token-budget estimation per problem, and anytime reasoning frameworks that produce improving solutions as more tokens are generated.((https://arxiv.org/abs/2603.12634|"Budget-Aware Value Tree Search for Token-Efficient LLM Reasoning" (2026)))
  
 ===== Budget-Aware Value Tree Search ===== ===== Budget-Aware Value Tree Search =====
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 ===== Token-Budget Estimation ===== ===== Token-Budget Estimation =====
  
-The TALE framework estimates per-problem token budgets based on reasoning complexity:+The TALE framework estimates per-problem token budgets based on reasoning complexity:((https://arxiv.org/abs/2412.18547|Han & Wang. "TALE: Token-Budget-Aware LLM Reasoning" (2024)))
  
 <latex>B_{optimal}(x) = f_{estimator}(x, \text{complexity}(x))</latex> <latex>B_{optimal}(x) = f_{estimator}(x, \text{complexity}(x))</latex>
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 ===== Anytime Reasoning with Early Stopping ===== ===== Anytime Reasoning with Early Stopping =====
  
-Anytime reasoning produces progressively improving solutions as more tokens are generated, enabling early termination when quality is sufficient or budget is exhausted.+Anytime reasoning produces progressively improving solutions as more tokens are generated, enabling early termination when quality is sufficient or budget is exhausted.((https://arxiv.org/abs/2601.11038|"Anytime Reasoning with Budget-Aware Self-Improvement" (2025)))
  
 **Anytime Index**: Quantifies quality improvement per added token: **Anytime Index**: Quantifies quality improvement per added token:
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 | TALE token-budget | Good (per-problem) | Slight accuracy drop | Low | | TALE token-budget | Good (per-problem) | Slight accuracy drop | Low |
 | Anytime + early stop | Best overall | Progressive improvement | Medium | | Anytime + early stop | Best overall | Progressive improvement | Medium |
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-===== References ===== 
- 
-  * [[https://arxiv.org/abs/2603.12634|"Budget-Aware Value Tree Search for Token-Efficient LLM Reasoning" (2026)]] 
-  * [[https://arxiv.org/abs/2601.11038|"Anytime Reasoning with Budget-Aware Self-Improvement" (2025)]] 
-  * [[https://arxiv.org/abs/2412.18547|Han & Wang. "TALE: Token-Budget-Aware LLM Reasoning" (2024)]] 
  
 ===== See Also ===== ===== See Also =====
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   * [[multi_hop_qa_agents|Multi-Hop QA Agents]]   * [[multi_hop_qa_agents|Multi-Hop QA Agents]]
   * [[financial_trading_agents|Financial Trading Agents]]   * [[financial_trading_agents|Financial Trading Agents]]
 +
 +===== References =====
  
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