alphapollo-reasoning

name	alphapollo-reasoning
title	AlphaApollo: Orchestrating Foundation Models and Tools for Reasoning
version	0.0.2
engine	skillxiv-v0.0.2-claude-opus-4.6
license	MIT
url	https://arxiv.org/abs/2510.06261
keywords	["agentic-reasoning","multi-turn-rl","tool-use","verification","iterative-refinement"]
description	Enable LLMs to solve complex problems through multi-turn agentic reasoning with tool-assisted verification and iterative refinement loops. Trigger: improve reasoning reliability on long-horizon tasks by combining RL with verification.

AlphaApollo: Orchestrating Models and Tools for Reliable Reasoning

Core Concept

AlphaApollo frames complex problem-solving as a multi-turn agentic process with three integrated levels: reasoning (multi-turn interactions), learning (turn-level RL), and evolution (multi-round refinement with verification). By separating actions from tool responses during RL training and implementing a propose-judge-update loop, the system achieves reliable reasoning on tasks requiring tool use, verification, and iterative correction.

The key insight: Decoupling actions from responses and learning from turn-level feedback enables models to build strategies that outlast individual tool failures.

Architecture Overview

• Multi-Turn Agentic Reasoning: Model reasons in turns, calling tools and receiving responses
• Turn-Level RL Training: Optimize tool-use decisions at each turn, not just final outcome
• Tool-Response Separation: Distinguish between model actions and external tool results
• Propose-Judge-Update Loop: Generate solution → verify with tools → refine based on feedback
• Long-Horizon Memory: Track prior attempts and insights across refinement rounds

Implementation Steps

1. Design the Agentic Interface

Define how the model interacts with tools and receives feedback.

class AgentAction:
    """Represents a single turn's action."""
    def __init__(self, action_type, content, tool_call=None):
        self.action_type = action_type  # "reason", "call_tool", "output"
        self.content = content  # Text of reasoning or tool name
        self.tool_call = tool_call  # Tool parameters if applicable

    def to_prompt(self):
        if self.action_type == "call_tool":
            return f"TOOL_CALL: ()"
        :
             


 :
    
     ():
        .tool_name = tool_name
        .status = status  
        .result = result
        .error = error

     ():
         .status == :
             
        :
             


 :
    
     ():
        .problem = problem
        .turns = []  
        .turn_rewards = []  
        .final_reward = 
        .solution = 

     ():
        .turns.append((action, response))
        .turn_rewards.append(immediate_reward)

     ():
        .final_reward = final_reward
        .solution = solution