lsrif-logic-structured-rl

name	lsrif-logic-structured-rl
title	LSRIF: Logic-Structured Reinforcement Learning for Instruction Following
version	0.0.2
engine	skillxiv-v0.0.2-claude-opus-4.6
license	MIT
url	https://arxiv.org/abs/2601.06431
keywords	["instruction-following","logic-constraints","structured-rewards","RL-training","semantic-understanding"]
description	Improves instruction-following by using differentiated reward mechanisms that recognize logical dependencies (sequential, conditional, parallel) in instructions, enabling better model reasoning about task structure.

Overview

Train agents to follow complex instructions that contain logical structures (sequential steps, conditional branches, parallel tasks). Rather than treating all instruction steps equally, apply structure-aware reward mechanisms that recognize when instructions have dependencies, enabling agents to learn the underlying logic.

When to Use

• For instruction-following tasks with complex logical structures
• When instructions have sequential, conditional, or parallel dependencies
• For improving out-of-domain generalization in instruction understanding
• When you need agents to reason about task structure, not just surface form

When NOT to Use

• For simple, single-action instructions without dependencies
• For domains where instruction structure is always flat/linear
• When you don't have labeled logical structure annotations
• For real-time applications where training time is critical

Key Technical Components

Logical Structure Annotation

Classify each instruction's logical type to enable targeted reward design.

# Instruction structure classification
class InstructionStructure:
    SEQUENTIAL = "sequential"    # Steps must occur in order
    PARALLEL = "parallel"        # Steps independent, order irrelevant
    CONDITIONAL = "conditional"  # Branch based on conditions

    @staticmethod
    def analyze(instruction):
        """Determine logical structure of instruction"""
        if has_order_dependencies(instruction):
            return InstructionStructure.SEQUENTIAL
        elif has_condition_branches(instruction):
            return InstructionStructure.CONDITIONAL
        elif all_steps_independent(instruction):
            return InstructionStructure.PARALLEL

def detect_structure(text):
    
    sequential_markers = [, , , ]
    parallel_markers = [, , , ]
    conditional_markers = [, , , ]

     (m  text  m  sequential_markers):
         
     (m  text  m  parallel_markers):
         
     (m  text  m  conditional_markers):